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hyperledger/indy-plenum | plenum/server/replica.py | Replica.validateCommit | def validateCommit(self, commit: Commit, sender: str) -> bool:
"""
Return whether the COMMIT specified is valid.
:param commit: the COMMIT to validate
:return: True if `request` is valid, False otherwise
"""
key = (commit.viewNo, commit.ppSeqNo)
if not self.has_prepared(key):
self.enqueue_commit(commit, sender)
return False
if self.commits.hasCommitFrom(commit, sender):
raise SuspiciousNode(sender, Suspicions.DUPLICATE_CM_SENT, commit)
# BLS multi-sig:
pre_prepare = self.getPrePrepare(commit.viewNo, commit.ppSeqNo)
why_not = self._bls_bft_replica.validate_commit(commit, sender, pre_prepare)
if why_not == BlsBftReplica.CM_BLS_SIG_WRONG:
self.logger.warning("{} discard Commit message from "
"{}:{}".format(self, sender, commit))
raise SuspiciousNode(sender,
Suspicions.CM_BLS_SIG_WRONG,
commit)
elif why_not is not None:
self.logger.warning("Unknown error code returned for bls commit "
"validation {}".format(why_not))
return True | python | def validateCommit(self, commit: Commit, sender: str) -> bool:
"""
Return whether the COMMIT specified is valid.
:param commit: the COMMIT to validate
:return: True if `request` is valid, False otherwise
"""
key = (commit.viewNo, commit.ppSeqNo)
if not self.has_prepared(key):
self.enqueue_commit(commit, sender)
return False
if self.commits.hasCommitFrom(commit, sender):
raise SuspiciousNode(sender, Suspicions.DUPLICATE_CM_SENT, commit)
# BLS multi-sig:
pre_prepare = self.getPrePrepare(commit.viewNo, commit.ppSeqNo)
why_not = self._bls_bft_replica.validate_commit(commit, sender, pre_prepare)
if why_not == BlsBftReplica.CM_BLS_SIG_WRONG:
self.logger.warning("{} discard Commit message from "
"{}:{}".format(self, sender, commit))
raise SuspiciousNode(sender,
Suspicions.CM_BLS_SIG_WRONG,
commit)
elif why_not is not None:
self.logger.warning("Unknown error code returned for bls commit "
"validation {}".format(why_not))
return True | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica.addToCommits | def addToCommits(self, commit: Commit, sender: str):
"""
Add the specified COMMIT to this replica's list of received
commit requests.
:param commit: the COMMIT to add to the list
:param sender: the name of the node that sent the COMMIT
"""
# BLS multi-sig:
self._bls_bft_replica.process_commit(commit, sender)
self.commits.addVote(commit, sender)
self.tryOrder(commit) | python | def addToCommits(self, commit: Commit, sender: str):
"""
Add the specified COMMIT to this replica's list of received
commit requests.
:param commit: the COMMIT to add to the list
:param sender: the name of the node that sent the COMMIT
"""
# BLS multi-sig:
self._bls_bft_replica.process_commit(commit, sender)
self.commits.addVote(commit, sender)
self.tryOrder(commit) | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica.canOrder | def canOrder(self, commit: Commit) -> Tuple[bool, Optional[str]]:
"""
Return whether the specified commitRequest can be returned to the node.
Decision criteria:
- If have got just n-f Commit requests then return request to node
- If less than n-f of commit requests then probably don't have
consensus on the request; don't return request to node
- If more than n-f then already returned to node; don't return request
to node
:param commit: the COMMIT
"""
quorum = self.quorums.commit.value
if not self.commits.hasQuorum(commit, quorum):
return False, "no quorum ({}): {} commits where f is {}". \
format(quorum, commit, self.f)
key = (commit.viewNo, commit.ppSeqNo)
if self.has_already_ordered(*key):
return False, "already ordered"
if commit.ppSeqNo > 1 and not self.all_prev_ordered(commit):
viewNo, ppSeqNo = commit.viewNo, commit.ppSeqNo
if viewNo not in self.stashed_out_of_order_commits:
self.stashed_out_of_order_commits[viewNo] = {}
self.stashed_out_of_order_commits[viewNo][ppSeqNo] = commit
self.startRepeating(self.process_stashed_out_of_order_commits,
self.config.PROCESS_STASHED_OUT_OF_ORDER_COMMITS_INTERVAL)
return False, "stashing {} since out of order". \
format(commit)
return True, None | python | def canOrder(self, commit: Commit) -> Tuple[bool, Optional[str]]:
"""
Return whether the specified commitRequest can be returned to the node.
Decision criteria:
- If have got just n-f Commit requests then return request to node
- If less than n-f of commit requests then probably don't have
consensus on the request; don't return request to node
- If more than n-f then already returned to node; don't return request
to node
:param commit: the COMMIT
"""
quorum = self.quorums.commit.value
if not self.commits.hasQuorum(commit, quorum):
return False, "no quorum ({}): {} commits where f is {}". \
format(quorum, commit, self.f)
key = (commit.viewNo, commit.ppSeqNo)
if self.has_already_ordered(*key):
return False, "already ordered"
if commit.ppSeqNo > 1 and not self.all_prev_ordered(commit):
viewNo, ppSeqNo = commit.viewNo, commit.ppSeqNo
if viewNo not in self.stashed_out_of_order_commits:
self.stashed_out_of_order_commits[viewNo] = {}
self.stashed_out_of_order_commits[viewNo][ppSeqNo] = commit
self.startRepeating(self.process_stashed_out_of_order_commits,
self.config.PROCESS_STASHED_OUT_OF_ORDER_COMMITS_INTERVAL)
return False, "stashing {} since out of order". \
format(commit)
return True, None | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica.all_prev_ordered | def all_prev_ordered(self, commit: Commit):
"""
Return True if all previous COMMITs have been ordered
"""
# TODO: This method does a lot of work, choose correct data
# structures to make it efficient.
viewNo, ppSeqNo = commit.viewNo, commit.ppSeqNo
if self.last_ordered_3pc == (viewNo, ppSeqNo - 1):
# Last ordered was in same view as this COMMIT
return True
# if some PREPAREs/COMMITs were completely missed in the same view
toCheck = set()
toCheck.update(set(self.sentPrePrepares.keys()))
toCheck.update(set(self.prePrepares.keys()))
toCheck.update(set(self.prepares.keys()))
toCheck.update(set(self.commits.keys()))
for (v, p) in toCheck:
if v < viewNo and (v, p) not in self.ordered:
# Have commits from previous view that are unordered.
return False
if v == viewNo and p < ppSeqNo and (v, p) not in self.ordered:
# If unordered commits are found with lower ppSeqNo then this
# cannot be ordered.
return False
return True | python | def all_prev_ordered(self, commit: Commit):
"""
Return True if all previous COMMITs have been ordered
"""
# TODO: This method does a lot of work, choose correct data
# structures to make it efficient.
viewNo, ppSeqNo = commit.viewNo, commit.ppSeqNo
if self.last_ordered_3pc == (viewNo, ppSeqNo - 1):
# Last ordered was in same view as this COMMIT
return True
# if some PREPAREs/COMMITs were completely missed in the same view
toCheck = set()
toCheck.update(set(self.sentPrePrepares.keys()))
toCheck.update(set(self.prePrepares.keys()))
toCheck.update(set(self.prepares.keys()))
toCheck.update(set(self.commits.keys()))
for (v, p) in toCheck:
if v < viewNo and (v, p) not in self.ordered:
# Have commits from previous view that are unordered.
return False
if v == viewNo and p < ppSeqNo and (v, p) not in self.ordered:
# If unordered commits are found with lower ppSeqNo then this
# cannot be ordered.
return False
return True | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica.process_checkpoint | def process_checkpoint(self, msg: Checkpoint, sender: str) -> bool:
"""
Process checkpoint messages
:return: whether processed (True) or stashed (False)
"""
self.logger.info('{} processing checkpoint {} from {}'.format(self, msg, sender))
result, reason = self.validator.validate_checkpoint_msg(msg)
if result == DISCARD:
self.discard(msg, "{} discard message {} from {} "
"with the reason: {}".format(self, msg, sender, reason),
self.logger.trace)
elif result == PROCESS:
self._do_process_checkpoint(msg, sender)
else:
self.logger.debug("{} stashing checkpoint message {} with "
"the reason: {}".format(self, msg, reason))
self.stasher.stash((msg, sender), result)
return False
return True | python | def process_checkpoint(self, msg: Checkpoint, sender: str) -> bool:
"""
Process checkpoint messages
:return: whether processed (True) or stashed (False)
"""
self.logger.info('{} processing checkpoint {} from {}'.format(self, msg, sender))
result, reason = self.validator.validate_checkpoint_msg(msg)
if result == DISCARD:
self.discard(msg, "{} discard message {} from {} "
"with the reason: {}".format(self, msg, sender, reason),
self.logger.trace)
elif result == PROCESS:
self._do_process_checkpoint(msg, sender)
else:
self.logger.debug("{} stashing checkpoint message {} with "
"the reason: {}".format(self, msg, reason))
self.stasher.stash((msg, sender), result)
return False
return True | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica._process_stashed_pre_prepare_for_time_if_possible | def _process_stashed_pre_prepare_for_time_if_possible(
self, key: Tuple[int, int]):
"""
Check if any PRE-PREPAREs that were stashed since their time was not
acceptable, can now be accepted since enough PREPAREs are received
"""
self.logger.debug('{} going to process stashed PRE-PREPAREs with '
'incorrect times'.format(self))
q = self.quorums.f
if len(self.preparesWaitingForPrePrepare[key]) > q:
times = [pr.ppTime for (pr, _) in
self.preparesWaitingForPrePrepare[key]]
most_common_time, freq = mostCommonElement(times)
if self.quorums.timestamp.is_reached(freq):
self.logger.debug('{} found sufficient PREPAREs for the '
'PRE-PREPARE{}'.format(self, key))
stashed_pp = self.pre_prepares_stashed_for_incorrect_time
pp, sender, done = stashed_pp[key]
if done:
self.logger.debug('{} already processed PRE-PREPARE{}'.format(self, key))
return True
# True is set since that will indicate to `is_pre_prepare_time_acceptable`
# that sufficient PREPAREs are received
stashed_pp[key] = (pp, sender, True)
self.process_three_phase_msg(pp, sender)
return True
return False | python | def _process_stashed_pre_prepare_for_time_if_possible(
self, key: Tuple[int, int]):
"""
Check if any PRE-PREPAREs that were stashed since their time was not
acceptable, can now be accepted since enough PREPAREs are received
"""
self.logger.debug('{} going to process stashed PRE-PREPAREs with '
'incorrect times'.format(self))
q = self.quorums.f
if len(self.preparesWaitingForPrePrepare[key]) > q:
times = [pr.ppTime for (pr, _) in
self.preparesWaitingForPrePrepare[key]]
most_common_time, freq = mostCommonElement(times)
if self.quorums.timestamp.is_reached(freq):
self.logger.debug('{} found sufficient PREPAREs for the '
'PRE-PREPARE{}'.format(self, key))
stashed_pp = self.pre_prepares_stashed_for_incorrect_time
pp, sender, done = stashed_pp[key]
if done:
self.logger.debug('{} already processed PRE-PREPARE{}'.format(self, key))
return True
# True is set since that will indicate to `is_pre_prepare_time_acceptable`
# that sufficient PREPAREs are received
stashed_pp[key] = (pp, sender, True)
self.process_three_phase_msg(pp, sender)
return True
return False | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica._remove_till_caught_up_3pc | def _remove_till_caught_up_3pc(self, last_caught_up_3PC):
"""
Remove any 3 phase messages till the last ordered key and also remove
any corresponding request keys
"""
outdated_pre_prepares = {}
for key, pp in self.prePrepares.items():
if compare_3PC_keys(key, last_caught_up_3PC) >= 0:
outdated_pre_prepares[key] = pp
for key, pp in self.sentPrePrepares.items():
if compare_3PC_keys(key, last_caught_up_3PC) >= 0:
outdated_pre_prepares[key] = pp
self.logger.trace('{} going to remove messages for {} 3PC keys'.format(
self, len(outdated_pre_prepares)))
for key, pp in outdated_pre_prepares.items():
self.batches.pop(key, None)
self.sentPrePrepares.pop(key, None)
self.prePrepares.pop(key, None)
self.prepares.pop(key, None)
self.commits.pop(key, None)
self._discard_ordered_req_keys(pp) | python | def _remove_till_caught_up_3pc(self, last_caught_up_3PC):
"""
Remove any 3 phase messages till the last ordered key and also remove
any corresponding request keys
"""
outdated_pre_prepares = {}
for key, pp in self.prePrepares.items():
if compare_3PC_keys(key, last_caught_up_3PC) >= 0:
outdated_pre_prepares[key] = pp
for key, pp in self.sentPrePrepares.items():
if compare_3PC_keys(key, last_caught_up_3PC) >= 0:
outdated_pre_prepares[key] = pp
self.logger.trace('{} going to remove messages for {} 3PC keys'.format(
self, len(outdated_pre_prepares)))
for key, pp in outdated_pre_prepares.items():
self.batches.pop(key, None)
self.sentPrePrepares.pop(key, None)
self.prePrepares.pop(key, None)
self.prepares.pop(key, None)
self.commits.pop(key, None)
self._discard_ordered_req_keys(pp) | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica._remove_ordered_from_queue | def _remove_ordered_from_queue(self, last_caught_up_3PC=None):
"""
Remove any Ordered that the replica might be sending to node which is
less than or equal to `last_caught_up_3PC` if `last_caught_up_3PC` is
passed else remove all ordered, needed in catchup
"""
to_remove = []
for i, msg in enumerate(self.outBox):
if isinstance(msg, Ordered) and \
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compare_3PC_keys((msg.viewNo, msg.ppSeqNo), last_caught_up_3PC) >= 0):
to_remove.append(i)
self.logger.trace('{} going to remove {} Ordered messages from outbox'.format(self, len(to_remove)))
# Removing Ordered from queue but returning `Ordered` in order that
# they should be processed.
removed = []
for i in reversed(to_remove):
removed.insert(0, self.outBox[i])
del self.outBox[i]
return removed | python | def _remove_ordered_from_queue(self, last_caught_up_3PC=None):
"""
Remove any Ordered that the replica might be sending to node which is
less than or equal to `last_caught_up_3PC` if `last_caught_up_3PC` is
passed else remove all ordered, needed in catchup
"""
to_remove = []
for i, msg in enumerate(self.outBox):
if isinstance(msg, Ordered) and \
(not last_caught_up_3PC or
compare_3PC_keys((msg.viewNo, msg.ppSeqNo), last_caught_up_3PC) >= 0):
to_remove.append(i)
self.logger.trace('{} going to remove {} Ordered messages from outbox'.format(self, len(to_remove)))
# Removing Ordered from queue but returning `Ordered` in order that
# they should be processed.
removed = []
for i in reversed(to_remove):
removed.insert(0, self.outBox[i])
del self.outBox[i]
return removed | [
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hyperledger/indy-plenum | plenum/server/replica.py | Replica._remove_stashed_checkpoints | def _remove_stashed_checkpoints(self, till_3pc_key=None):
"""
Remove stashed received checkpoints up to `till_3pc_key` if provided,
otherwise remove all stashed received checkpoints
"""
if till_3pc_key is None:
self.stashedRecvdCheckpoints.clear()
self.logger.info('{} removing all stashed checkpoints'.format(self))
return
for view_no in list(self.stashedRecvdCheckpoints.keys()):
if view_no < till_3pc_key[0]:
self.logger.info('{} removing stashed checkpoints for view {}'.format(self, view_no))
del self.stashedRecvdCheckpoints[view_no]
elif view_no == till_3pc_key[0]:
for (s, e) in list(self.stashedRecvdCheckpoints[view_no].keys()):
if e <= till_3pc_key[1]:
self.logger.info('{} removing stashed checkpoints: '
'viewNo={}, seqNoStart={}, seqNoEnd={}'.
format(self, view_no, s, e))
del self.stashedRecvdCheckpoints[view_no][(s, e)]
if len(self.stashedRecvdCheckpoints[view_no]) == 0:
del self.stashedRecvdCheckpoints[view_no] | python | def _remove_stashed_checkpoints(self, till_3pc_key=None):
"""
Remove stashed received checkpoints up to `till_3pc_key` if provided,
otherwise remove all stashed received checkpoints
"""
if till_3pc_key is None:
self.stashedRecvdCheckpoints.clear()
self.logger.info('{} removing all stashed checkpoints'.format(self))
return
for view_no in list(self.stashedRecvdCheckpoints.keys()):
if view_no < till_3pc_key[0]:
self.logger.info('{} removing stashed checkpoints for view {}'.format(self, view_no))
del self.stashedRecvdCheckpoints[view_no]
elif view_no == till_3pc_key[0]:
for (s, e) in list(self.stashedRecvdCheckpoints[view_no].keys()):
if e <= till_3pc_key[1]:
self.logger.info('{} removing stashed checkpoints: '
'viewNo={}, seqNoStart={}, seqNoEnd={}'.
format(self, view_no, s, e))
del self.stashedRecvdCheckpoints[view_no][(s, e)]
if len(self.stashedRecvdCheckpoints[view_no]) == 0:
del self.stashedRecvdCheckpoints[view_no] | [
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hyperledger/indy-plenum | stp_core/network/util.py | checkPortAvailable | def checkPortAvailable(ha):
"""Checks whether the given port is available"""
# Not sure why OS would allow binding to one type and not other.
# Checking for port available for TCP and UDP.
sockTypes = (socket.SOCK_DGRAM, socket.SOCK_STREAM)
for typ in sockTypes:
sock = socket.socket(socket.AF_INET, typ)
try:
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind(ha)
if typ == socket.SOCK_STREAM:
l_onoff = 1
l_linger = 0
sock.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER,
struct.pack('ii', l_onoff, l_linger))
except OSError as exc:
if exc.errno in [
errno.EADDRINUSE, errno.EADDRNOTAVAIL,
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WS_SOCKET_BIND_ERROR_NOT_AVAILABLE
]:
raise PortNotAvailable(ha)
else:
raise exc
finally:
sock.close() | python | def checkPortAvailable(ha):
"""Checks whether the given port is available"""
# Not sure why OS would allow binding to one type and not other.
# Checking for port available for TCP and UDP.
sockTypes = (socket.SOCK_DGRAM, socket.SOCK_STREAM)
for typ in sockTypes:
sock = socket.socket(socket.AF_INET, typ)
try:
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind(ha)
if typ == socket.SOCK_STREAM:
l_onoff = 1
l_linger = 0
sock.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER,
struct.pack('ii', l_onoff, l_linger))
except OSError as exc:
if exc.errno in [
errno.EADDRINUSE, errno.EADDRNOTAVAIL,
WS_SOCKET_BIND_ERROR_ALREADY_IN_USE,
WS_SOCKET_BIND_ERROR_NOT_AVAILABLE
]:
raise PortNotAvailable(ha)
else:
raise exc
finally:
sock.close() | [
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hyperledger/indy-plenum | stp_core/network/util.py | evenCompare | def evenCompare(a: str, b: str) -> bool:
"""
A deterministic but more evenly distributed comparator than simple alphabetical.
Useful when comparing consecutive strings and an even distribution is needed.
Provides an even chance of returning true as often as false
"""
ab = a.encode('utf-8')
bb = b.encode('utf-8')
ac = crypto_hash_sha256(ab)
bc = crypto_hash_sha256(bb)
return ac < bc | python | def evenCompare(a: str, b: str) -> bool:
"""
A deterministic but more evenly distributed comparator than simple alphabetical.
Useful when comparing consecutive strings and an even distribution is needed.
Provides an even chance of returning true as often as false
"""
ab = a.encode('utf-8')
bb = b.encode('utf-8')
ac = crypto_hash_sha256(ab)
bc = crypto_hash_sha256(bb)
return ac < bc | [
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kylejusticemagnuson/pyti | pyti/keltner_bands.py | center_band | def center_band(close_data, high_data, low_data, period):
"""
Center Band.
Formula:
CB = SMA(TP)
"""
tp = typical_price(close_data, high_data, low_data)
cb = sma(tp, period)
return cb | python | def center_band(close_data, high_data, low_data, period):
"""
Center Band.
Formula:
CB = SMA(TP)
"""
tp = typical_price(close_data, high_data, low_data)
cb = sma(tp, period)
return cb | [
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kylejusticemagnuson/pyti | pyti/simple_moving_average.py | simple_moving_average | def simple_moving_average(data, period):
"""
Simple Moving Average.
Formula:
SUM(data / N)
"""
catch_errors.check_for_period_error(data, period)
# Mean of Empty Slice RuntimeWarning doesn't affect output so it is
# supressed
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
sma = [np.mean(data[idx-(period-1):idx+1]) for idx in range(0, len(data))]
sma = fill_for_noncomputable_vals(data, sma)
return sma | python | def simple_moving_average(data, period):
"""
Simple Moving Average.
Formula:
SUM(data / N)
"""
catch_errors.check_for_period_error(data, period)
# Mean of Empty Slice RuntimeWarning doesn't affect output so it is
# supressed
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
sma = [np.mean(data[idx-(period-1):idx+1]) for idx in range(0, len(data))]
sma = fill_for_noncomputable_vals(data, sma)
return sma | [
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kylejusticemagnuson/pyti | pyti/average_true_range_percent.py | average_true_range_percent | def average_true_range_percent(close_data, period):
"""
Average True Range Percent.
Formula:
ATRP = (ATR / CLOSE) * 100
"""
catch_errors.check_for_period_error(close_data, period)
atrp = (atr(close_data, period) / np.array(close_data)) * 100
return atrp | python | def average_true_range_percent(close_data, period):
"""
Average True Range Percent.
Formula:
ATRP = (ATR / CLOSE) * 100
"""
catch_errors.check_for_period_error(close_data, period)
atrp = (atr(close_data, period) / np.array(close_data)) * 100
return atrp | [
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kylejusticemagnuson/pyti | pyti/on_balance_volume.py | on_balance_volume | def on_balance_volume(close_data, volume):
"""
On Balance Volume.
Formula:
start = 1
if CLOSEt > CLOSEt-1
obv = obvt-1 + volumet
elif CLOSEt < CLOSEt-1
obv = obvt-1 - volumet
elif CLOSEt == CLOSTt-1
obv = obvt-1
"""
catch_errors.check_for_input_len_diff(close_data, volume)
obv = np.zeros(len(volume))
obv[0] = 1
for idx in range(1, len(obv)):
if close_data[idx] > close_data[idx-1]:
obv[idx] = obv[idx-1] + volume[idx]
elif close_data[idx] < close_data[idx-1]:
obv[idx] = obv[idx-1] - volume[idx]
elif close_data[idx] == close_data[idx-1]:
obv[idx] = obv[idx-1]
return obv | python | def on_balance_volume(close_data, volume):
"""
On Balance Volume.
Formula:
start = 1
if CLOSEt > CLOSEt-1
obv = obvt-1 + volumet
elif CLOSEt < CLOSEt-1
obv = obvt-1 - volumet
elif CLOSEt == CLOSTt-1
obv = obvt-1
"""
catch_errors.check_for_input_len_diff(close_data, volume)
obv = np.zeros(len(volume))
obv[0] = 1
for idx in range(1, len(obv)):
if close_data[idx] > close_data[idx-1]:
obv[idx] = obv[idx-1] + volume[idx]
elif close_data[idx] < close_data[idx-1]:
obv[idx] = obv[idx-1] - volume[idx]
elif close_data[idx] == close_data[idx-1]:
obv[idx] = obv[idx-1]
return obv | [
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kylejusticemagnuson/pyti | pyti/rate_of_change.py | rate_of_change | def rate_of_change(data, period):
"""
Rate of Change.
Formula:
(Close - Close n periods ago) / (Close n periods ago) * 100
"""
catch_errors.check_for_period_error(data, period)
rocs = [((data[idx] - data[idx - (period - 1)]) /
data[idx - (period - 1)]) * 100 for idx in range(period - 1, len(data))]
rocs = fill_for_noncomputable_vals(data, rocs)
return rocs | python | def rate_of_change(data, period):
"""
Rate of Change.
Formula:
(Close - Close n periods ago) / (Close n periods ago) * 100
"""
catch_errors.check_for_period_error(data, period)
rocs = [((data[idx] - data[idx - (period - 1)]) /
data[idx - (period - 1)]) * 100 for idx in range(period - 1, len(data))]
rocs = fill_for_noncomputable_vals(data, rocs)
return rocs | [
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kylejusticemagnuson/pyti | pyti/average_true_range.py | average_true_range | def average_true_range(close_data, period):
"""
Average True Range.
Formula:
ATRt = ATRt-1 * (n - 1) + TRt / n
"""
tr = true_range(close_data, period)
atr = smoothed_moving_average(tr, period)
atr[0:period-1] = tr[0:period-1]
return atr | python | def average_true_range(close_data, period):
"""
Average True Range.
Formula:
ATRt = ATRt-1 * (n - 1) + TRt / n
"""
tr = true_range(close_data, period)
atr = smoothed_moving_average(tr, period)
atr[0:period-1] = tr[0:period-1]
return atr | [
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kylejusticemagnuson/pyti | pyti/relative_strength_index.py | relative_strength_index | def relative_strength_index(data, period):
"""
Relative Strength Index.
Formula:
RSI = 100 - (100 / 1 + (prevGain/prevLoss))
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
changes = [data_tup[1] - data_tup[0] for data_tup in zip(data[::1], data[1::1])]
filtered_gain = [val < 0 for val in changes]
gains = [0 if filtered_gain[idx] is True else changes[idx] for idx in range(0, len(filtered_gain))]
filtered_loss = [val > 0 for val in changes]
losses = [0 if filtered_loss[idx] is True else abs(changes[idx]) for idx in range(0, len(filtered_loss))]
avg_gain = np.mean(gains[:period])
avg_loss = np.mean(losses[:period])
rsi = []
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
for idx in range(1, len(data) - period):
avg_gain = ((avg_gain * (period - 1) +
gains[idx + (period - 1)]) / period)
avg_loss = ((avg_loss * (period - 1) +
losses[idx + (period - 1)]) / period)
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
rsi = fill_for_noncomputable_vals(data, rsi)
return rsi | python | def relative_strength_index(data, period):
"""
Relative Strength Index.
Formula:
RSI = 100 - (100 / 1 + (prevGain/prevLoss))
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
changes = [data_tup[1] - data_tup[0] for data_tup in zip(data[::1], data[1::1])]
filtered_gain = [val < 0 for val in changes]
gains = [0 if filtered_gain[idx] is True else changes[idx] for idx in range(0, len(filtered_gain))]
filtered_loss = [val > 0 for val in changes]
losses = [0 if filtered_loss[idx] is True else abs(changes[idx]) for idx in range(0, len(filtered_loss))]
avg_gain = np.mean(gains[:period])
avg_loss = np.mean(losses[:period])
rsi = []
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
for idx in range(1, len(data) - period):
avg_gain = ((avg_gain * (period - 1) +
gains[idx + (period - 1)]) / period)
avg_loss = ((avg_loss * (period - 1) +
losses[idx + (period - 1)]) / period)
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
rsi = fill_for_noncomputable_vals(data, rsi)
return rsi | [
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kylejusticemagnuson/pyti | pyti/vertical_horizontal_filter.py | vertical_horizontal_filter | def vertical_horizontal_filter(data, period):
"""
Vertical Horizontal Filter.
Formula:
ABS(pHIGH - pLOW) / SUM(ABS(Pi - Pi-1))
"""
catch_errors.check_for_period_error(data, period)
vhf = [abs(np.max(data[idx+1-period:idx+1]) -
np.min(data[idx+1-period:idx+1])) /
sum([abs(data[idx+1-period:idx+1][i] - data[idx+1-period:idx+1][i-1]) for i in range(0, len(data[idx+1-period:idx+1]))]) for idx in range(period - 1, len(data))]
vhf = fill_for_noncomputable_vals(data, vhf)
return vhf | python | def vertical_horizontal_filter(data, period):
"""
Vertical Horizontal Filter.
Formula:
ABS(pHIGH - pLOW) / SUM(ABS(Pi - Pi-1))
"""
catch_errors.check_for_period_error(data, period)
vhf = [abs(np.max(data[idx+1-period:idx+1]) -
np.min(data[idx+1-period:idx+1])) /
sum([abs(data[idx+1-period:idx+1][i] - data[idx+1-period:idx+1][i-1]) for i in range(0, len(data[idx+1-period:idx+1]))]) for idx in range(period - 1, len(data))]
vhf = fill_for_noncomputable_vals(data, vhf)
return vhf | [
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Formula:
ABS(pHIGH - pLOW) / SUM(ABS(Pi - Pi-1)) | [
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kylejusticemagnuson/pyti | pyti/ultimate_oscillator.py | buying_pressure | def buying_pressure(close_data, low_data):
"""
Buying Pressure.
Formula:
BP = current close - min()
"""
catch_errors.check_for_input_len_diff(close_data, low_data)
bp = [close_data[idx] - np.min([low_data[idx], close_data[idx-1]]) for idx in range(1, len(close_data))]
bp = fill_for_noncomputable_vals(close_data, bp)
return bp | python | def buying_pressure(close_data, low_data):
"""
Buying Pressure.
Formula:
BP = current close - min()
"""
catch_errors.check_for_input_len_diff(close_data, low_data)
bp = [close_data[idx] - np.min([low_data[idx], close_data[idx-1]]) for idx in range(1, len(close_data))]
bp = fill_for_noncomputable_vals(close_data, bp)
return bp | [
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kylejusticemagnuson/pyti | pyti/ultimate_oscillator.py | ultimate_oscillator | def ultimate_oscillator(close_data, low_data):
"""
Ultimate Oscillator.
Formula:
UO = 100 * ((4 * AVG7) + (2 * AVG14) + AVG28) / (4 + 2 + 1)
"""
a7 = 4 * average_7(close_data, low_data)
a14 = 2 * average_14(close_data, low_data)
a28 = average_28(close_data, low_data)
uo = 100 * ((a7 + a14 + a28) / 7)
return uo | python | def ultimate_oscillator(close_data, low_data):
"""
Ultimate Oscillator.
Formula:
UO = 100 * ((4 * AVG7) + (2 * AVG14) + AVG28) / (4 + 2 + 1)
"""
a7 = 4 * average_7(close_data, low_data)
a14 = 2 * average_14(close_data, low_data)
a28 = average_28(close_data, low_data)
uo = 100 * ((a7 + a14 + a28) / 7)
return uo | [
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kylejusticemagnuson/pyti | pyti/aroon.py | aroon_up | def aroon_up(data, period):
"""
Aroon Up.
Formula:
AROONUP = (((PERIOD) - (PERIODS since PERIOD high)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_up = [((period -
list(reversed(data[idx+1-period:idx+1])).index(np.max(data[idx+1-period:idx+1]))) /
float(period)) * 100 for idx in range(period-1, len(data))]
a_up = fill_for_noncomputable_vals(data, a_up)
return a_up | python | def aroon_up(data, period):
"""
Aroon Up.
Formula:
AROONUP = (((PERIOD) - (PERIODS since PERIOD high)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_up = [((period -
list(reversed(data[idx+1-period:idx+1])).index(np.max(data[idx+1-period:idx+1]))) /
float(period)) * 100 for idx in range(period-1, len(data))]
a_up = fill_for_noncomputable_vals(data, a_up)
return a_up | [
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Formula:
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kylejusticemagnuson/pyti | pyti/aroon.py | aroon_down | def aroon_down(data, period):
"""
Aroon Down.
Formula:
AROONDWN = (((PERIOD) - (PERIODS SINCE PERIOD LOW)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_down = [((period -
list(reversed(data[idx+1-period:idx+1])).index(np.min(data[idx+1-period:idx+1]))) /
float(period)) * 100 for idx in range(period-1, len(data))]
a_down = fill_for_noncomputable_vals(data, a_down)
return a_down | python | def aroon_down(data, period):
"""
Aroon Down.
Formula:
AROONDWN = (((PERIOD) - (PERIODS SINCE PERIOD LOW)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_down = [((period -
list(reversed(data[idx+1-period:idx+1])).index(np.min(data[idx+1-period:idx+1]))) /
float(period)) * 100 for idx in range(period-1, len(data))]
a_down = fill_for_noncomputable_vals(data, a_down)
return a_down | [
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kylejusticemagnuson/pyti | pyti/price_channels.py | upper_price_channel | def upper_price_channel(data, period, upper_percent):
"""
Upper Price Channel.
Formula:
upc = EMA(t) * (1 + upper_percent / 100)
"""
catch_errors.check_for_period_error(data, period)
emas = ema(data, period)
upper_channel = [val * (1+float(upper_percent)/100) for val in emas]
return upper_channel | python | def upper_price_channel(data, period, upper_percent):
"""
Upper Price Channel.
Formula:
upc = EMA(t) * (1 + upper_percent / 100)
"""
catch_errors.check_for_period_error(data, period)
emas = ema(data, period)
upper_channel = [val * (1+float(upper_percent)/100) for val in emas]
return upper_channel | [
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Formula:
upc = EMA(t) * (1 + upper_percent / 100) | [
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kylejusticemagnuson/pyti | pyti/price_channels.py | lower_price_channel | def lower_price_channel(data, period, lower_percent):
"""
Lower Price Channel.
Formula:
lpc = EMA(t) * (1 - lower_percent / 100)
"""
catch_errors.check_for_period_error(data, period)
emas = ema(data, period)
lower_channel = [val * (1-float(lower_percent)/100) for val in emas]
return lower_channel | python | def lower_price_channel(data, period, lower_percent):
"""
Lower Price Channel.
Formula:
lpc = EMA(t) * (1 - lower_percent / 100)
"""
catch_errors.check_for_period_error(data, period)
emas = ema(data, period)
lower_channel = [val * (1-float(lower_percent)/100) for val in emas]
return lower_channel | [
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Formula:
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kylejusticemagnuson/pyti | pyti/exponential_moving_average.py | exponential_moving_average | def exponential_moving_average(data, period):
"""
Exponential Moving Average.
Formula:
p0 + (1 - w) * p1 + (1 - w)^2 * p2 + (1 + w)^3 * p3 +...
/ 1 + (1 - w) + (1 - w)^2 + (1 - w)^3 +...
where: w = 2 / (N + 1)
"""
catch_errors.check_for_period_error(data, period)
emas = [exponential_moving_average_helper(
data[idx - period + 1:idx + 1], period) for idx in range(period - 1, len(data))]
emas = fill_for_noncomputable_vals(data, emas)
return emas | python | def exponential_moving_average(data, period):
"""
Exponential Moving Average.
Formula:
p0 + (1 - w) * p1 + (1 - w)^2 * p2 + (1 + w)^3 * p3 +...
/ 1 + (1 - w) + (1 - w)^2 + (1 - w)^3 +...
where: w = 2 / (N + 1)
"""
catch_errors.check_for_period_error(data, period)
emas = [exponential_moving_average_helper(
data[idx - period + 1:idx + 1], period) for idx in range(period - 1, len(data))]
emas = fill_for_noncomputable_vals(data, emas)
return emas | [
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kylejusticemagnuson/pyti | pyti/commodity_channel_index.py | commodity_channel_index | def commodity_channel_index(close_data, high_data, low_data, period):
"""
Commodity Channel Index.
Formula:
CCI = (TP - SMA(TP)) / (0.015 * Mean Deviation)
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
catch_errors.check_for_period_error(close_data, period)
tp = typical_price(close_data, high_data, low_data)
cci = ((tp - sma(tp, period)) /
(0.015 * np.mean(np.absolute(tp - np.mean(tp)))))
return cci | python | def commodity_channel_index(close_data, high_data, low_data, period):
"""
Commodity Channel Index.
Formula:
CCI = (TP - SMA(TP)) / (0.015 * Mean Deviation)
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
catch_errors.check_for_period_error(close_data, period)
tp = typical_price(close_data, high_data, low_data)
cci = ((tp - sma(tp, period)) /
(0.015 * np.mean(np.absolute(tp - np.mean(tp)))))
return cci | [
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kylejusticemagnuson/pyti | pyti/williams_percent_r.py | williams_percent_r | def williams_percent_r(close_data):
"""
Williams %R.
Formula:
wr = (HighestHigh - close / HighestHigh - LowestLow) * -100
"""
highest_high = np.max(close_data)
lowest_low = np.min(close_data)
wr = [((highest_high - close) / (highest_high - lowest_low)) * -100 for close in close_data]
return wr | python | def williams_percent_r(close_data):
"""
Williams %R.
Formula:
wr = (HighestHigh - close / HighestHigh - LowestLow) * -100
"""
highest_high = np.max(close_data)
lowest_low = np.min(close_data)
wr = [((highest_high - close) / (highest_high - lowest_low)) * -100 for close in close_data]
return wr | [
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kylejusticemagnuson/pyti | pyti/moving_average_convergence_divergence.py | moving_average_convergence_divergence | def moving_average_convergence_divergence(data, short_period, long_period):
"""
Moving Average Convergence Divergence.
Formula:
EMA(DATA, P1) - EMA(DATA, P2)
"""
catch_errors.check_for_period_error(data, short_period)
catch_errors.check_for_period_error(data, long_period)
macd = ema(data, short_period) - ema(data, long_period)
return macd | python | def moving_average_convergence_divergence(data, short_period, long_period):
"""
Moving Average Convergence Divergence.
Formula:
EMA(DATA, P1) - EMA(DATA, P2)
"""
catch_errors.check_for_period_error(data, short_period)
catch_errors.check_for_period_error(data, long_period)
macd = ema(data, short_period) - ema(data, long_period)
return macd | [
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Formula:
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kylejusticemagnuson/pyti | pyti/money_flow_index.py | money_flow_index | def money_flow_index(close_data, high_data, low_data, volume, period):
"""
Money Flow Index.
Formula:
MFI = 100 - (100 / (1 + PMF / NMF))
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
catch_errors.check_for_period_error(close_data, period)
mf = money_flow(close_data, high_data, low_data, volume)
tp = typical_price(close_data, high_data, low_data)
flow = [tp[idx] > tp[idx-1] for idx in range(1, len(tp))]
pf = [mf[idx] if flow[idx] else 0 for idx in range(0, len(flow))]
nf = [mf[idx] if not flow[idx] else 0 for idx in range(0, len(flow))]
pmf = [sum(pf[idx+1-period:idx+1]) for idx in range(period-1, len(pf))]
nmf = [sum(nf[idx+1-period:idx+1]) for idx in range(period-1, len(nf))]
# Dividing by 0 is not an issue, it turns the value into NaN which we would
# want in that case
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
money_ratio = np.array(pmf) / np.array(nmf)
mfi = 100 - (100 / (1 + money_ratio))
mfi = fill_for_noncomputable_vals(close_data, mfi)
return mfi | python | def money_flow_index(close_data, high_data, low_data, volume, period):
"""
Money Flow Index.
Formula:
MFI = 100 - (100 / (1 + PMF / NMF))
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
catch_errors.check_for_period_error(close_data, period)
mf = money_flow(close_data, high_data, low_data, volume)
tp = typical_price(close_data, high_data, low_data)
flow = [tp[idx] > tp[idx-1] for idx in range(1, len(tp))]
pf = [mf[idx] if flow[idx] else 0 for idx in range(0, len(flow))]
nf = [mf[idx] if not flow[idx] else 0 for idx in range(0, len(flow))]
pmf = [sum(pf[idx+1-period:idx+1]) for idx in range(period-1, len(pf))]
nmf = [sum(nf[idx+1-period:idx+1]) for idx in range(period-1, len(nf))]
# Dividing by 0 is not an issue, it turns the value into NaN which we would
# want in that case
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
money_ratio = np.array(pmf) / np.array(nmf)
mfi = 100 - (100 / (1 + money_ratio))
mfi = fill_for_noncomputable_vals(close_data, mfi)
return mfi | [
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kylejusticemagnuson/pyti | pyti/typical_price.py | typical_price | def typical_price(close_data, high_data, low_data):
"""
Typical Price.
Formula:
TPt = (HIGHt + LOWt + CLOSEt) / 3
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
tp = [(high_data[idx] + low_data[idx] + close_data[idx]) / 3 for idx in range(0, len(close_data))]
return np.array(tp) | python | def typical_price(close_data, high_data, low_data):
"""
Typical Price.
Formula:
TPt = (HIGHt + LOWt + CLOSEt) / 3
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
tp = [(high_data[idx] + low_data[idx] + close_data[idx]) / 3 for idx in range(0, len(close_data))]
return np.array(tp) | [
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kylejusticemagnuson/pyti | pyti/true_range.py | true_range | def true_range(close_data, period):
"""
True Range.
Formula:
TRt = MAX(abs(Ht - Lt), abs(Ht - Ct-1), abs(Lt - Ct-1))
"""
catch_errors.check_for_period_error(close_data, period)
tr = [np.max([np.max(close_data[idx+1-period:idx+1]) -
np.min(close_data[idx+1-period:idx+1]),
abs(np.max(close_data[idx+1-period:idx+1]) -
close_data[idx-1]),
abs(np.min(close_data[idx+1-period:idx+1]) -
close_data[idx-1])]) for idx in range(period-1, len(close_data))]
tr = fill_for_noncomputable_vals(close_data, tr)
return tr | python | def true_range(close_data, period):
"""
True Range.
Formula:
TRt = MAX(abs(Ht - Lt), abs(Ht - Ct-1), abs(Lt - Ct-1))
"""
catch_errors.check_for_period_error(close_data, period)
tr = [np.max([np.max(close_data[idx+1-period:idx+1]) -
np.min(close_data[idx+1-period:idx+1]),
abs(np.max(close_data[idx+1-period:idx+1]) -
close_data[idx-1]),
abs(np.min(close_data[idx+1-period:idx+1]) -
close_data[idx-1])]) for idx in range(period-1, len(close_data))]
tr = fill_for_noncomputable_vals(close_data, tr)
return tr | [
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kylejusticemagnuson/pyti | pyti/double_smoothed_stochastic.py | double_smoothed_stochastic | def double_smoothed_stochastic(data, period):
"""
Double Smoothed Stochastic.
Formula:
dss = 100 * EMA(Close - Lowest Low) / EMA(Highest High - Lowest Low)
"""
catch_errors.check_for_period_error(data, period)
lows = [data[idx] - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_lows = ema(ema(lows, period), period)
highs = [np.max(data[idx+1-period:idx+1]) - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_highs = ema(ema(highs, period), period)
dss = (sm_lows / sm_highs) * 100
dss = fill_for_noncomputable_vals(data, dss)
return dss | python | def double_smoothed_stochastic(data, period):
"""
Double Smoothed Stochastic.
Formula:
dss = 100 * EMA(Close - Lowest Low) / EMA(Highest High - Lowest Low)
"""
catch_errors.check_for_period_error(data, period)
lows = [data[idx] - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_lows = ema(ema(lows, period), period)
highs = [np.max(data[idx+1-period:idx+1]) - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_highs = ema(ema(highs, period), period)
dss = (sm_lows / sm_highs) * 100
dss = fill_for_noncomputable_vals(data, dss)
return dss | [
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kylejusticemagnuson/pyti | pyti/volume_adjusted_moving_average.py | volume_adjusted_moving_average | def volume_adjusted_moving_average(close_data, volume, period):
"""
Volume Adjusted Moving Average.
Formula:
VAMA = SUM(CLOSE * VolumeRatio) / period
"""
catch_errors.check_for_input_len_diff(close_data, volume)
catch_errors.check_for_period_error(close_data, period)
avg_vol = np.mean(volume)
vol_incr = avg_vol * 0.67
vol_ratio = [val / vol_incr for val in volume]
close_vol = np.array(close_data) * vol_ratio
vama = [sum(close_vol[idx+1-period:idx+1]) / period for idx in range(period-1, len(close_data))]
vama = fill_for_noncomputable_vals(close_data, vama)
return vama | python | def volume_adjusted_moving_average(close_data, volume, period):
"""
Volume Adjusted Moving Average.
Formula:
VAMA = SUM(CLOSE * VolumeRatio) / period
"""
catch_errors.check_for_input_len_diff(close_data, volume)
catch_errors.check_for_period_error(close_data, period)
avg_vol = np.mean(volume)
vol_incr = avg_vol * 0.67
vol_ratio = [val / vol_incr for val in volume]
close_vol = np.array(close_data) * vol_ratio
vama = [sum(close_vol[idx+1-period:idx+1]) / period for idx in range(period-1, len(close_data))]
vama = fill_for_noncomputable_vals(close_data, vama)
return vama | [
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Formula:
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kylejusticemagnuson/pyti | pyti/double_exponential_moving_average.py | double_exponential_moving_average | def double_exponential_moving_average(data, period):
"""
Double Exponential Moving Average.
Formula:
DEMA = 2*EMA - EMA(EMA)
"""
catch_errors.check_for_period_error(data, period)
dema = (2 * ema(data, period)) - ema(ema(data, period), period)
return dema | python | def double_exponential_moving_average(data, period):
"""
Double Exponential Moving Average.
Formula:
DEMA = 2*EMA - EMA(EMA)
"""
catch_errors.check_for_period_error(data, period)
dema = (2 * ema(data, period)) - ema(ema(data, period), period)
return dema | [
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kylejusticemagnuson/pyti | pyti/triangular_moving_average.py | triangular_moving_average | def triangular_moving_average(data, period):
"""
Triangular Moving Average.
Formula:
TMA = SMA(SMA())
"""
catch_errors.check_for_period_error(data, period)
tma = sma(sma(data, period), period)
return tma | python | def triangular_moving_average(data, period):
"""
Triangular Moving Average.
Formula:
TMA = SMA(SMA())
"""
catch_errors.check_for_period_error(data, period)
tma = sma(sma(data, period), period)
return tma | [
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kylejusticemagnuson/pyti | pyti/weighted_moving_average.py | weighted_moving_average | def weighted_moving_average(data, period):
"""
Weighted Moving Average.
Formula:
(P1 + 2 P2 + 3 P3 + ... + n Pn) / K
where K = (1+2+...+n) = n(n+1)/2 and Pn is the most recent price
"""
catch_errors.check_for_period_error(data, period)
k = (period * (period + 1)) / 2.0
wmas = []
for idx in range(0, len(data)-period+1):
product = [data[idx + period_idx] * (period_idx + 1) for period_idx in range(0, period)]
wma = sum(product) / k
wmas.append(wma)
wmas = fill_for_noncomputable_vals(data, wmas)
return wmas | python | def weighted_moving_average(data, period):
"""
Weighted Moving Average.
Formula:
(P1 + 2 P2 + 3 P3 + ... + n Pn) / K
where K = (1+2+...+n) = n(n+1)/2 and Pn is the most recent price
"""
catch_errors.check_for_period_error(data, period)
k = (period * (period + 1)) / 2.0
wmas = []
for idx in range(0, len(data)-period+1):
product = [data[idx + period_idx] * (period_idx + 1) for period_idx in range(0, period)]
wma = sum(product) / k
wmas.append(wma)
wmas = fill_for_noncomputable_vals(data, wmas)
return wmas | [
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kylejusticemagnuson/pyti | pyti/ichimoku_cloud.py | conversion_base_line_helper | def conversion_base_line_helper(data, period):
"""
The only real difference between TenkanSen and KijunSen is the period value
"""
catch_errors.check_for_period_error(data, period)
cblh = [(np.max(data[idx+1-period:idx+1]) +
np.min(data[idx+1-period:idx+1])) / 2 for idx in range(period-1, len(data))]
cblh = fill_for_noncomputable_vals(data, cblh)
return cblh | python | def conversion_base_line_helper(data, period):
"""
The only real difference between TenkanSen and KijunSen is the period value
"""
catch_errors.check_for_period_error(data, period)
cblh = [(np.max(data[idx+1-period:idx+1]) +
np.min(data[idx+1-period:idx+1])) / 2 for idx in range(period-1, len(data))]
cblh = fill_for_noncomputable_vals(data, cblh)
return cblh | [
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kylejusticemagnuson/pyti | pyti/chande_momentum_oscillator.py | chande_momentum_oscillator | def chande_momentum_oscillator(close_data, period):
"""
Chande Momentum Oscillator.
Formula:
cmo = 100 * ((sum_up - sum_down) / (sum_up + sum_down))
"""
catch_errors.check_for_period_error(close_data, period)
close_data = np.array(close_data)
moving_period_diffs = [[(close_data[idx+1-period:idx+1][i] -
close_data[idx+1-period:idx+1][i-1]) for i in range(1, len(close_data[idx+1-period:idx+1]))] for idx in range(0, len(close_data))]
sum_up = []
sum_down = []
for period_diffs in moving_period_diffs:
ups = [val if val > 0 else 0 for val in period_diffs]
sum_up.append(sum(ups))
downs = [abs(val) if val < 0 else 0 for val in period_diffs]
sum_down.append(sum(downs))
sum_up = np.array(sum_up)
sum_down = np.array(sum_down)
# numpy is able to handle dividing by zero and makes those calculations
# nans which is what we want, so we safely suppress the RuntimeWarning
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
cmo = 100 * ((sum_up - sum_down) / (sum_up + sum_down))
return cmo | python | def chande_momentum_oscillator(close_data, period):
"""
Chande Momentum Oscillator.
Formula:
cmo = 100 * ((sum_up - sum_down) / (sum_up + sum_down))
"""
catch_errors.check_for_period_error(close_data, period)
close_data = np.array(close_data)
moving_period_diffs = [[(close_data[idx+1-period:idx+1][i] -
close_data[idx+1-period:idx+1][i-1]) for i in range(1, len(close_data[idx+1-period:idx+1]))] for idx in range(0, len(close_data))]
sum_up = []
sum_down = []
for period_diffs in moving_period_diffs:
ups = [val if val > 0 else 0 for val in period_diffs]
sum_up.append(sum(ups))
downs = [abs(val) if val < 0 else 0 for val in period_diffs]
sum_down.append(sum(downs))
sum_up = np.array(sum_up)
sum_down = np.array(sum_down)
# numpy is able to handle dividing by zero and makes those calculations
# nans which is what we want, so we safely suppress the RuntimeWarning
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
cmo = 100 * ((sum_up - sum_down) / (sum_up + sum_down))
return cmo | [
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kylejusticemagnuson/pyti | pyti/price_oscillator.py | price_oscillator | def price_oscillator(data, short_period, long_period):
"""
Price Oscillator.
Formula:
(short EMA - long EMA / long EMA) * 100
"""
catch_errors.check_for_period_error(data, short_period)
catch_errors.check_for_period_error(data, long_period)
ema_short = ema(data, short_period)
ema_long = ema(data, long_period)
po = ((ema_short - ema_long) / ema_long) * 100
return po | python | def price_oscillator(data, short_period, long_period):
"""
Price Oscillator.
Formula:
(short EMA - long EMA / long EMA) * 100
"""
catch_errors.check_for_period_error(data, short_period)
catch_errors.check_for_period_error(data, long_period)
ema_short = ema(data, short_period)
ema_long = ema(data, long_period)
po = ((ema_short - ema_long) / ema_long) * 100
return po | [
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kylejusticemagnuson/pyti | pyti/catch_errors.py | check_for_period_error | def check_for_period_error(data, period):
"""
Check for Period Error.
This method checks if the developer is trying to enter a period that is
larger than the data set being entered. If that is the case an exception is
raised with a custom message that informs the developer that their period
is greater than the data set.
"""
period = int(period)
data_len = len(data)
if data_len < period:
raise Exception("Error: data_len < period") | python | def check_for_period_error(data, period):
"""
Check for Period Error.
This method checks if the developer is trying to enter a period that is
larger than the data set being entered. If that is the case an exception is
raised with a custom message that informs the developer that their period
is greater than the data set.
"""
period = int(period)
data_len = len(data)
if data_len < period:
raise Exception("Error: data_len < period") | [
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kylejusticemagnuson/pyti | pyti/catch_errors.py | check_for_input_len_diff | def check_for_input_len_diff(*args):
"""
Check for Input Length Difference.
This method checks if multiple data sets that are inputted are all the same
size. If they are not the same length an error is raised with a custom
message that informs the developer that the data set's lengths are not the
same.
"""
arrays_len = [len(arr) for arr in args]
if not all(a == arrays_len[0] for a in arrays_len):
err_msg = ("Error: mismatched data lengths, check to ensure that all "
"input data is the same length and valid")
raise Exception(err_msg) | python | def check_for_input_len_diff(*args):
"""
Check for Input Length Difference.
This method checks if multiple data sets that are inputted are all the same
size. If they are not the same length an error is raised with a custom
message that informs the developer that the data set's lengths are not the
same.
"""
arrays_len = [len(arr) for arr in args]
if not all(a == arrays_len[0] for a in arrays_len):
err_msg = ("Error: mismatched data lengths, check to ensure that all "
"input data is the same length and valid")
raise Exception(err_msg) | [
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kylejusticemagnuson/pyti | pyti/bollinger_bands.py | upper_bollinger_band | def upper_bollinger_band(data, period, std_mult=2.0):
"""
Upper Bollinger Band.
Formula:
u_bb = SMA(t) + STD(SMA(t-n:t)) * std_mult
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
simple_ma = sma(data, period)[period-1:]
upper_bb = []
for idx in range(len(data) - period + 1):
std_dev = np.std(data[idx:idx + period])
upper_bb.append(simple_ma[idx] + std_dev * std_mult)
upper_bb = fill_for_noncomputable_vals(data, upper_bb)
return np.array(upper_bb) | python | def upper_bollinger_band(data, period, std_mult=2.0):
"""
Upper Bollinger Band.
Formula:
u_bb = SMA(t) + STD(SMA(t-n:t)) * std_mult
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
simple_ma = sma(data, period)[period-1:]
upper_bb = []
for idx in range(len(data) - period + 1):
std_dev = np.std(data[idx:idx + period])
upper_bb.append(simple_ma[idx] + std_dev * std_mult)
upper_bb = fill_for_noncomputable_vals(data, upper_bb)
return np.array(upper_bb) | [
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kylejusticemagnuson/pyti | pyti/bollinger_bands.py | middle_bollinger_band | def middle_bollinger_band(data, period, std=2.0):
"""
Middle Bollinger Band.
Formula:
m_bb = sma()
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
mid_bb = sma(data, period)
return mid_bb | python | def middle_bollinger_band(data, period, std=2.0):
"""
Middle Bollinger Band.
Formula:
m_bb = sma()
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
mid_bb = sma(data, period)
return mid_bb | [
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kylejusticemagnuson/pyti | pyti/bollinger_bands.py | lower_bollinger_band | def lower_bollinger_band(data, period, std=2.0):
"""
Lower Bollinger Band.
Formula:
u_bb = SMA(t) - STD(SMA(t-n:t)) * std_mult
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
simple_ma = sma(data, period)[period-1:]
lower_bb = []
for idx in range(len(data) - period + 1):
std_dev = np.std(data[idx:idx + period])
lower_bb.append(simple_ma[idx] - std_dev * std)
lower_bb = fill_for_noncomputable_vals(data, lower_bb)
return np.array(lower_bb) | python | def lower_bollinger_band(data, period, std=2.0):
"""
Lower Bollinger Band.
Formula:
u_bb = SMA(t) - STD(SMA(t-n:t)) * std_mult
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
simple_ma = sma(data, period)[period-1:]
lower_bb = []
for idx in range(len(data) - period + 1):
std_dev = np.std(data[idx:idx + period])
lower_bb.append(simple_ma[idx] - std_dev * std)
lower_bb = fill_for_noncomputable_vals(data, lower_bb)
return np.array(lower_bb) | [
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kylejusticemagnuson/pyti | pyti/bollinger_bands.py | percent_bandwidth | def percent_bandwidth(data, period, std=2.0):
"""
Percent Bandwidth.
Formula:
%_bw = data() - l_bb() / bb_range()
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
percent_bandwidth = ((np.array(data) -
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bb_range(data, period, std)
)
return percent_bandwidth | python | def percent_bandwidth(data, period, std=2.0):
"""
Percent Bandwidth.
Formula:
%_bw = data() - l_bb() / bb_range()
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
percent_bandwidth = ((np.array(data) -
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return percent_bandwidth | [
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kylejusticemagnuson/pyti | pyti/standard_deviation.py | standard_deviation | def standard_deviation(data, period):
"""
Standard Deviation.
Formula:
std = sqrt(avg(abs(x - avg(x))^2))
"""
catch_errors.check_for_period_error(data, period)
stds = [np.std(data[idx+1-period:idx+1], ddof=1) for idx in range(period-1, len(data))]
stds = fill_for_noncomputable_vals(data, stds)
return stds | python | def standard_deviation(data, period):
"""
Standard Deviation.
Formula:
std = sqrt(avg(abs(x - avg(x))^2))
"""
catch_errors.check_for_period_error(data, period)
stds = [np.std(data[idx+1-period:idx+1], ddof=1) for idx in range(period-1, len(data))]
stds = fill_for_noncomputable_vals(data, stds)
return stds | [
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Formula:
std = sqrt(avg(abs(x - avg(x))^2)) | [
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kylejusticemagnuson/pyti | pyti/detrended_price_oscillator.py | detrended_price_oscillator | def detrended_price_oscillator(data, period):
"""
Detrended Price Oscillator.
Formula:
DPO = DATA[i] - Avg(DATA[period/2 + 1])
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
dop = [data[idx] - np.mean(data[idx+1-(int(period/2)+1):idx+1]) for idx in range(period-1, len(data))]
dop = fill_for_noncomputable_vals(data, dop)
return dop | python | def detrended_price_oscillator(data, period):
"""
Detrended Price Oscillator.
Formula:
DPO = DATA[i] - Avg(DATA[period/2 + 1])
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
dop = [data[idx] - np.mean(data[idx+1-(int(period/2)+1):idx+1]) for idx in range(period-1, len(data))]
dop = fill_for_noncomputable_vals(data, dop)
return dop | [
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kylejusticemagnuson/pyti | pyti/smoothed_moving_average.py | smoothed_moving_average | def smoothed_moving_average(data, period):
"""
Smoothed Moving Average.
Formula:
smma = avg(data(n)) - avg(data(n)/n) + data(t)/n
"""
catch_errors.check_for_period_error(data, period)
series = pd.Series(data)
return series.ewm(alpha = 1.0/period).mean().values.flatten() | python | def smoothed_moving_average(data, period):
"""
Smoothed Moving Average.
Formula:
smma = avg(data(n)) - avg(data(n)/n) + data(t)/n
"""
catch_errors.check_for_period_error(data, period)
series = pd.Series(data)
return series.ewm(alpha = 1.0/period).mean().values.flatten() | [
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kylejusticemagnuson/pyti | pyti/chaikin_money_flow.py | chaikin_money_flow | def chaikin_money_flow(close_data, high_data, low_data, volume, period):
"""
Chaikin Money Flow.
Formula:
CMF = SUM[(((Cn - Ln) - (Hn - Cn)) / (Hn - Ln)) * V] / SUM(Vn)
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume)
catch_errors.check_for_period_error(close_data, period)
close_data = np.array(close_data)
high_data = np.array(high_data)
low_data = np.array(low_data)
volume = np.array(volume)
cmf = [sum((((close_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1]) -
(high_data[idx+1-period:idx+1] - close_data[idx+1-period:idx+1])) /
(high_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1])) *
volume[idx+1-period:idx+1]) / sum(volume[idx+1-period:idx+1]) for idx in range(period-1, len(close_data))]
cmf = fill_for_noncomputable_vals(close_data, cmf)
return cmf | python | def chaikin_money_flow(close_data, high_data, low_data, volume, period):
"""
Chaikin Money Flow.
Formula:
CMF = SUM[(((Cn - Ln) - (Hn - Cn)) / (Hn - Ln)) * V] / SUM(Vn)
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume)
catch_errors.check_for_period_error(close_data, period)
close_data = np.array(close_data)
high_data = np.array(high_data)
low_data = np.array(low_data)
volume = np.array(volume)
cmf = [sum((((close_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1]) -
(high_data[idx+1-period:idx+1] - close_data[idx+1-period:idx+1])) /
(high_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1])) *
volume[idx+1-period:idx+1]) / sum(volume[idx+1-period:idx+1]) for idx in range(period-1, len(close_data))]
cmf = fill_for_noncomputable_vals(close_data, cmf)
return cmf | [
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kylejusticemagnuson/pyti | pyti/hull_moving_average.py | hull_moving_average | def hull_moving_average(data, period):
"""
Hull Moving Average.
Formula:
HMA = WMA(2*WMA(n/2) - WMA(n)), sqrt(n)
"""
catch_errors.check_for_period_error(data, period)
hma = wma(
2 * wma(data, int(period/2)) - wma(data, period), int(np.sqrt(period))
)
return hma | python | def hull_moving_average(data, period):
"""
Hull Moving Average.
Formula:
HMA = WMA(2*WMA(n/2) - WMA(n)), sqrt(n)
"""
catch_errors.check_for_period_error(data, period)
hma = wma(
2 * wma(data, int(period/2)) - wma(data, period), int(np.sqrt(period))
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return hma | [
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kylejusticemagnuson/pyti | pyti/standard_variance.py | standard_variance | def standard_variance(data, period):
"""
Standard Variance.
Formula:
(Ct - AVGt)^2 / N
"""
catch_errors.check_for_period_error(data, period)
sv = [np.var(data[idx+1-period:idx+1], ddof=1) for idx in range(period-1, len(data))]
sv = fill_for_noncomputable_vals(data, sv)
return sv | python | def standard_variance(data, period):
"""
Standard Variance.
Formula:
(Ct - AVGt)^2 / N
"""
catch_errors.check_for_period_error(data, period)
sv = [np.var(data[idx+1-period:idx+1], ddof=1) for idx in range(period-1, len(data))]
sv = fill_for_noncomputable_vals(data, sv)
return sv | [
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kylejusticemagnuson/pyti | pyti/directional_indicators.py | calculate_up_moves | def calculate_up_moves(high_data):
"""
Up Move.
Formula:
UPMOVE = Ht - Ht-1
"""
up_moves = [high_data[idx] - high_data[idx-1] for idx in range(1, len(high_data))]
return [np.nan] + up_moves | python | def calculate_up_moves(high_data):
"""
Up Move.
Formula:
UPMOVE = Ht - Ht-1
"""
up_moves = [high_data[idx] - high_data[idx-1] for idx in range(1, len(high_data))]
return [np.nan] + up_moves | [
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kylejusticemagnuson/pyti | pyti/directional_indicators.py | calculate_down_moves | def calculate_down_moves(low_data):
"""
Down Move.
Formula:
DWNMOVE = Lt-1 - Lt
"""
down_moves = [low_data[idx-1] - low_data[idx] for idx in range(1, len(low_data))]
return [np.nan] + down_moves | python | def calculate_down_moves(low_data):
"""
Down Move.
Formula:
DWNMOVE = Lt-1 - Lt
"""
down_moves = [low_data[idx-1] - low_data[idx] for idx in range(1, len(low_data))]
return [np.nan] + down_moves | [
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kylejusticemagnuson/pyti | pyti/directional_indicators.py | average_directional_index | def average_directional_index(close_data, high_data, low_data, period):
"""
Average Directional Index.
Formula:
ADX = 100 * SMMA(abs((+DI - -DI) / (+DI + -DI)))
"""
avg_di = (abs(
(positive_directional_index(
close_data, high_data, low_data, period) -
negative_directional_index(
close_data, high_data, low_data, period)) /
(positive_directional_index(
close_data, high_data, low_data, period) +
negative_directional_index(
close_data, high_data, low_data, period)))
)
adx = 100 * smma(avg_di, period)
return adx | python | def average_directional_index(close_data, high_data, low_data, period):
"""
Average Directional Index.
Formula:
ADX = 100 * SMMA(abs((+DI - -DI) / (+DI + -DI)))
"""
avg_di = (abs(
(positive_directional_index(
close_data, high_data, low_data, period) -
negative_directional_index(
close_data, high_data, low_data, period)) /
(positive_directional_index(
close_data, high_data, low_data, period) +
negative_directional_index(
close_data, high_data, low_data, period)))
)
adx = 100 * smma(avg_di, period)
return adx | [
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kylejusticemagnuson/pyti | pyti/linear_weighted_moving_average.py | linear_weighted_moving_average | def linear_weighted_moving_average(data, period):
"""
Linear Weighted Moving Average.
Formula:
LWMA = SUM(DATA[i]) * i / SUM(i)
"""
catch_errors.check_for_period_error(data, period)
idx_period = list(range(1, period+1))
lwma = [(sum([i * idx_period[data[idx-(period-1):idx+1].index(i)]
for i in data[idx-(period-1):idx+1]])) /
sum(range(1, len(data[idx+1-period:idx+1])+1)) for idx in range(period-1, len(data))]
lwma = fill_for_noncomputable_vals(data, lwma)
return lwma | python | def linear_weighted_moving_average(data, period):
"""
Linear Weighted Moving Average.
Formula:
LWMA = SUM(DATA[i]) * i / SUM(i)
"""
catch_errors.check_for_period_error(data, period)
idx_period = list(range(1, period+1))
lwma = [(sum([i * idx_period[data[idx-(period-1):idx+1].index(i)]
for i in data[idx-(period-1):idx+1]])) /
sum(range(1, len(data[idx+1-period:idx+1])+1)) for idx in range(period-1, len(data))]
lwma = fill_for_noncomputable_vals(data, lwma)
return lwma | [
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kylejusticemagnuson/pyti | pyti/volume_oscillator.py | volume_oscillator | def volume_oscillator(volume, short_period, long_period):
"""
Volume Oscillator.
Formula:
vo = 100 * (SMA(vol, short) - SMA(vol, long) / SMA(vol, long))
"""
catch_errors.check_for_period_error(volume, short_period)
catch_errors.check_for_period_error(volume, long_period)
vo = (100 * ((sma(volume, short_period) - sma(volume, long_period)) /
sma(volume, long_period)))
return vo | python | def volume_oscillator(volume, short_period, long_period):
"""
Volume Oscillator.
Formula:
vo = 100 * (SMA(vol, short) - SMA(vol, long) / SMA(vol, long))
"""
catch_errors.check_for_period_error(volume, short_period)
catch_errors.check_for_period_error(volume, long_period)
vo = (100 * ((sma(volume, short_period) - sma(volume, long_period)) /
sma(volume, long_period)))
return vo | [
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kylejusticemagnuson/pyti | pyti/triple_exponential_moving_average.py | triple_exponential_moving_average | def triple_exponential_moving_average(data, period):
"""
Triple Exponential Moving Average.
Formula:
TEMA = (3*EMA - 3*EMA(EMA)) + EMA(EMA(EMA))
"""
catch_errors.check_for_period_error(data, period)
tema = ((3 * ema(data, period) - (3 * ema(ema(data, period), period))) +
ema(ema(ema(data, period), period), period)
)
return tema | python | def triple_exponential_moving_average(data, period):
"""
Triple Exponential Moving Average.
Formula:
TEMA = (3*EMA - 3*EMA(EMA)) + EMA(EMA(EMA))
"""
catch_errors.check_for_period_error(data, period)
tema = ((3 * ema(data, period) - (3 * ema(ema(data, period), period))) +
ema(ema(ema(data, period), period), period)
)
return tema | [
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kylejusticemagnuson/pyti | pyti/money_flow.py | money_flow | def money_flow(close_data, high_data, low_data, volume):
"""
Money Flow.
Formula:
MF = VOLUME * TYPICAL PRICE
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
mf = volume * tp(close_data, high_data, low_data)
return mf | python | def money_flow(close_data, high_data, low_data, volume):
"""
Money Flow.
Formula:
MF = VOLUME * TYPICAL PRICE
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
mf = volume * tp(close_data, high_data, low_data)
return mf | [
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mrooney/mintapi | mintapi/api.py | Mint.request_and_check | def request_and_check(self, url, method='get',
expected_content_type=None, **kwargs):
"""Performs a request, and checks that the status is OK, and that the
content-type matches expectations.
Args:
url: URL to request
method: either 'get' or 'post'
expected_content_type: prefix to match response content-type against
**kwargs: passed to the request method directly.
Raises:
RuntimeError if status_code does not match.
"""
assert method in ['get', 'post']
result = self.driver.request(method, url, **kwargs)
if result.status_code != requests.codes.ok:
raise RuntimeError('Error requesting %r, status = %d' %
(url, result.status_code))
if expected_content_type is not None:
content_type = result.headers.get('content-type', '')
if not re.match(expected_content_type, content_type):
raise RuntimeError(
'Error requesting %r, content type %r does not match %r' %
(url, content_type, expected_content_type))
return result | python | def request_and_check(self, url, method='get',
expected_content_type=None, **kwargs):
"""Performs a request, and checks that the status is OK, and that the
content-type matches expectations.
Args:
url: URL to request
method: either 'get' or 'post'
expected_content_type: prefix to match response content-type against
**kwargs: passed to the request method directly.
Raises:
RuntimeError if status_code does not match.
"""
assert method in ['get', 'post']
result = self.driver.request(method, url, **kwargs)
if result.status_code != requests.codes.ok:
raise RuntimeError('Error requesting %r, status = %d' %
(url, result.status_code))
if expected_content_type is not None:
content_type = result.headers.get('content-type', '')
if not re.match(expected_content_type, content_type):
raise RuntimeError(
'Error requesting %r, content type %r does not match %r' %
(url, content_type, expected_content_type))
return result | [
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mrooney/mintapi | mintapi/api.py | Mint.get_transactions_json | def get_transactions_json(self, include_investment=False,
skip_duplicates=False, start_date=None, id=0):
"""Returns the raw JSON transaction data as downloaded from Mint. The JSON
transaction data includes some additional information missing from the
CSV data, such as whether the transaction is pending or completed, but
leaves off the year for current year transactions.
Warning: In order to reliably include or exclude duplicates, it is
necessary to change the user account property 'hide_duplicates' to the
appropriate value. This affects what is displayed in the web
interface. Note that the CSV transactions never exclude duplicates.
"""
# Warning: This is a global property for the user that we are changing.
self.set_user_property(
'hide_duplicates', 'T' if skip_duplicates else 'F')
# Converts the start date into datetime format - must be mm/dd/yy
try:
start_date = datetime.strptime(start_date, '%m/%d/%y')
except (TypeError, ValueError):
start_date = None
all_txns = []
offset = 0
# Mint only returns some of the transactions at once. To get all of
# them, we have to keep asking for more until we reach the end.
while 1:
url = MINT_ROOT_URL + '/getJsonData.xevent'
params = {
'queryNew': '',
'offset': offset,
'comparableType': '8',
'rnd': Mint.get_rnd(),
}
# Specifying accountId=0 causes Mint to return investment
# transactions as well. Otherwise they are skipped by
# default.
if id > 0 or include_investment:
params['id'] = id
if include_investment:
params['task'] = 'transactions'
else:
params['task'] = 'transactions,txnfilters'
params['filterType'] = 'cash'
result = self.request_and_check(
url, headers=JSON_HEADER, params=params,
expected_content_type='text/json|application/json')
data = json.loads(result.text)
txns = data['set'][0].get('data', [])
if not txns:
break
if start_date:
last_dt = json_date_to_datetime(txns[-1]['odate'])
if last_dt < start_date:
keep_txns = [
t for t in txns
if json_date_to_datetime(t['odate']) >= start_date]
all_txns.extend(keep_txns)
break
all_txns.extend(txns)
offset += len(txns)
return all_txns | python | def get_transactions_json(self, include_investment=False,
skip_duplicates=False, start_date=None, id=0):
"""Returns the raw JSON transaction data as downloaded from Mint. The JSON
transaction data includes some additional information missing from the
CSV data, such as whether the transaction is pending or completed, but
leaves off the year for current year transactions.
Warning: In order to reliably include or exclude duplicates, it is
necessary to change the user account property 'hide_duplicates' to the
appropriate value. This affects what is displayed in the web
interface. Note that the CSV transactions never exclude duplicates.
"""
# Warning: This is a global property for the user that we are changing.
self.set_user_property(
'hide_duplicates', 'T' if skip_duplicates else 'F')
# Converts the start date into datetime format - must be mm/dd/yy
try:
start_date = datetime.strptime(start_date, '%m/%d/%y')
except (TypeError, ValueError):
start_date = None
all_txns = []
offset = 0
# Mint only returns some of the transactions at once. To get all of
# them, we have to keep asking for more until we reach the end.
while 1:
url = MINT_ROOT_URL + '/getJsonData.xevent'
params = {
'queryNew': '',
'offset': offset,
'comparableType': '8',
'rnd': Mint.get_rnd(),
}
# Specifying accountId=0 causes Mint to return investment
# transactions as well. Otherwise they are skipped by
# default.
if id > 0 or include_investment:
params['id'] = id
if include_investment:
params['task'] = 'transactions'
else:
params['task'] = 'transactions,txnfilters'
params['filterType'] = 'cash'
result = self.request_and_check(
url, headers=JSON_HEADER, params=params,
expected_content_type='text/json|application/json')
data = json.loads(result.text)
txns = data['set'][0].get('data', [])
if not txns:
break
if start_date:
last_dt = json_date_to_datetime(txns[-1]['odate'])
if last_dt < start_date:
keep_txns = [
t for t in txns
if json_date_to_datetime(t['odate']) >= start_date]
all_txns.extend(keep_txns)
break
all_txns.extend(txns)
offset += len(txns)
return all_txns | [
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Warning: In order to reliably include or exclude duplicates, it is
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mrooney/mintapi | mintapi/api.py | Mint.get_detailed_transactions | def get_detailed_transactions(self, include_investment=False,
skip_duplicates=False,
remove_pending=True,
start_date=None):
"""Returns the JSON transaction data as a DataFrame, and converts
current year dates and prior year dates into consistent datetime
format, and reverses credit activity.
Note: start_date must be in format mm/dd/yy. If pulls take too long,
use a more recent start date. See json explanations of
include_investment and skip_duplicates.
Also note: Mint includes pending transactions, however these sometimes
change dates/amounts after the transactions post. They have been
removed by default in this pull, but can be included by changing
remove_pending to False
"""
assert_pd()
result = self.get_transactions_json(include_investment,
skip_duplicates, start_date)
df = pd.DataFrame(result)
df['odate'] = df['odate'].apply(json_date_to_datetime)
if remove_pending:
df = df[~df.isPending]
df.reset_index(drop=True, inplace=True)
df.amount = df.apply(reverse_credit_amount, axis=1)
return df | python | def get_detailed_transactions(self, include_investment=False,
skip_duplicates=False,
remove_pending=True,
start_date=None):
"""Returns the JSON transaction data as a DataFrame, and converts
current year dates and prior year dates into consistent datetime
format, and reverses credit activity.
Note: start_date must be in format mm/dd/yy. If pulls take too long,
use a more recent start date. See json explanations of
include_investment and skip_duplicates.
Also note: Mint includes pending transactions, however these sometimes
change dates/amounts after the transactions post. They have been
removed by default in this pull, but can be included by changing
remove_pending to False
"""
assert_pd()
result = self.get_transactions_json(include_investment,
skip_duplicates, start_date)
df = pd.DataFrame(result)
df['odate'] = df['odate'].apply(json_date_to_datetime)
if remove_pending:
df = df[~df.isPending]
df.reset_index(drop=True, inplace=True)
df.amount = df.apply(reverse_credit_amount, axis=1)
return df | [
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Also note: Mint includes pending transactions, however these sometimes
change dates/amounts after the transactions post. They have been
removed by default in this pull, but can be included by changing
remove_pending to False | [
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mrooney/mintapi | mintapi/api.py | Mint.get_transactions_csv | def get_transactions_csv(self, include_investment=False, acct=0):
"""Returns the raw CSV transaction data as downloaded from Mint.
If include_investment == True, also includes transactions that Mint
classifies as investment-related. You may find that the investment
transaction data is not sufficiently detailed to actually be useful,
however.
"""
# Specifying accountId=0 causes Mint to return investment
# transactions as well. Otherwise they are skipped by
# default.
params = None
if include_investment or acct > 0:
params = {'accountId': acct}
result = self.request_and_check(
'{}/transactionDownload.event'.format(MINT_ROOT_URL),
params=params,
expected_content_type='text/csv')
return result.content | python | def get_transactions_csv(self, include_investment=False, acct=0):
"""Returns the raw CSV transaction data as downloaded from Mint.
If include_investment == True, also includes transactions that Mint
classifies as investment-related. You may find that the investment
transaction data is not sufficiently detailed to actually be useful,
however.
"""
# Specifying accountId=0 causes Mint to return investment
# transactions as well. Otherwise they are skipped by
# default.
params = None
if include_investment or acct > 0:
params = {'accountId': acct}
result = self.request_and_check(
'{}/transactionDownload.event'.format(MINT_ROOT_URL),
params=params,
expected_content_type='text/csv')
return result.content | [
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mrooney/mintapi | mintapi/api.py | Mint.get_transactions | def get_transactions(self, include_investment=False):
"""Returns the transaction data as a Pandas DataFrame."""
assert_pd()
s = StringIO(self.get_transactions_csv(
include_investment=include_investment))
s.seek(0)
df = pd.read_csv(s, parse_dates=['Date'])
df.columns = [c.lower().replace(' ', '_') for c in df.columns]
df.category = (df.category.str.lower()
.replace('uncategorized', pd.np.nan))
return df | python | def get_transactions(self, include_investment=False):
"""Returns the transaction data as a Pandas DataFrame."""
assert_pd()
s = StringIO(self.get_transactions_csv(
include_investment=include_investment))
s.seek(0)
df = pd.read_csv(s, parse_dates=['Date'])
df.columns = [c.lower().replace(' ', '_') for c in df.columns]
df.category = (df.category.str.lower()
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return df | [
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StellarCN/py-stellar-base | stellar_base/address.py | Address.payments | def payments(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the payments JSON from this instance's Horizon server.
Retrieve the payments JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_payments(address=self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def payments(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the payments JSON from this instance's Horizon server.
Retrieve the payments JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_payments(address=self.address, cursor=cursor, order=order, limit=limit, sse=sse) | [
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StellarCN/py-stellar-base | stellar_base/address.py | Address.offers | def offers(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the offers JSON from this instance's Horizon server.
Retrieve the offers JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_offers(self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def offers(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the offers JSON from this instance's Horizon server.
Retrieve the offers JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_offers(self.address, cursor=cursor, order=order, limit=limit, sse=sse) | [
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StellarCN/py-stellar-base | stellar_base/address.py | Address.transactions | def transactions(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the transactions JSON from this instance's Horizon server.
Retrieve the transactions JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_transactions(
self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def transactions(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the transactions JSON from this instance's Horizon server.
Retrieve the transactions JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
"""
return self.horizon.account_transactions(
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StellarCN/py-stellar-base | stellar_base/address.py | Address.operations | def operations(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the operations JSON from this instance's Horizon server.
Retrieve the operations JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_operations(
self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def operations(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the operations JSON from this instance's Horizon server.
Retrieve the operations JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_operations(
self.address, cursor=cursor, order=order, limit=limit, sse=sse) | [
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StellarCN/py-stellar-base | stellar_base/address.py | Address.trades | def trades(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the trades JSON from this instance's Horizon server.
Retrieve the trades JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_trades(
self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def trades(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the trades JSON from this instance's Horizon server.
Retrieve the trades JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_trades(
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StellarCN/py-stellar-base | stellar_base/address.py | Address.effects | def effects(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the effects JSON from this instance's Horizon server.
Retrieve the effects JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_effects(
self.address, cursor=cursor, order=order, limit=limit, sse=sse) | python | def effects(self, cursor=None, order='asc', limit=10, sse=False):
"""Retrieve the effects JSON from this instance's Horizon server.
Retrieve the effects JSON response for the account associated with
this :class:`Address`.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use the SSE client for connecting to Horizon.
"""
return self.horizon.account_effects(
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.submit | def submit(self, te):
"""Submit the transaction using a pooled connection, and retry on failure.
`POST /transactions
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-create.html>`_
Uses form-encoded data to send over to Horizon.
:return: The JSON response indicating the success/failure of the
submitted transaction.
:rtype: dict
"""
params = {'tx': te}
url = urljoin(self.horizon_uri, 'transactions/')
# POST is not included in Retry's method_whitelist for a good reason.
# our custom retry mechanism follows
reply = None
retry_count = self.num_retries
while True:
try:
reply = self._session.post(
url, data=params, timeout=self.request_timeout)
return check_horizon_reply(reply.json())
except (RequestException, NewConnectionError, ValueError) as e:
if reply is not None:
msg = 'Horizon submit exception: {}, reply: [{}] {}'.format(
str(e), reply.status_code, reply.text)
else:
msg = 'Horizon submit exception: {}'.format(str(e))
logging.warning(msg)
if (reply is not None and reply.status_code not in self.status_forcelist) or retry_count <= 0:
if reply is None:
raise HorizonRequestError(e)
raise HorizonError('Invalid horizon reply: [{}] {}'.format(
reply.status_code, reply.text), reply.status_code)
retry_count -= 1
logging.warning('Submit retry attempt {}'.format(retry_count))
sleep(self.backoff_factor) | python | def submit(self, te):
"""Submit the transaction using a pooled connection, and retry on failure.
`POST /transactions
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-create.html>`_
Uses form-encoded data to send over to Horizon.
:return: The JSON response indicating the success/failure of the
submitted transaction.
:rtype: dict
"""
params = {'tx': te}
url = urljoin(self.horizon_uri, 'transactions/')
# POST is not included in Retry's method_whitelist for a good reason.
# our custom retry mechanism follows
reply = None
retry_count = self.num_retries
while True:
try:
reply = self._session.post(
url, data=params, timeout=self.request_timeout)
return check_horizon_reply(reply.json())
except (RequestException, NewConnectionError, ValueError) as e:
if reply is not None:
msg = 'Horizon submit exception: {}, reply: [{}] {}'.format(
str(e), reply.status_code, reply.text)
else:
msg = 'Horizon submit exception: {}'.format(str(e))
logging.warning(msg)
if (reply is not None and reply.status_code not in self.status_forcelist) or retry_count <= 0:
if reply is None:
raise HorizonRequestError(e)
raise HorizonError('Invalid horizon reply: [{}] {}'.format(
reply.status_code, reply.text), reply.status_code)
retry_count -= 1
logging.warning('Submit retry attempt {}'.format(retry_count))
sleep(self.backoff_factor) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.account | def account(self, address):
"""Returns information and links relating to a single account.
`GET /accounts/{account}
<https://www.stellar.org/developers/horizon/reference/endpoints/accounts-single.html>`_
:param str address: The account ID to retrieve details about.
:return: The account details in a JSON response.
:rtype: dict
"""
endpoint = '/accounts/{account_id}'.format(account_id=address)
return self.query(endpoint) | python | def account(self, address):
"""Returns information and links relating to a single account.
`GET /accounts/{account}
<https://www.stellar.org/developers/horizon/reference/endpoints/accounts-single.html>`_
:param str address: The account ID to retrieve details about.
:return: The account details in a JSON response.
:rtype: dict
"""
endpoint = '/accounts/{account_id}'.format(account_id=address)
return self.query(endpoint) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.account_data | def account_data(self, address, key):
"""This endpoint represents a single data associated with a given
account.
`GET /accounts/{account}/data/{key}
<https://www.stellar.org/developers/horizon/reference/endpoints/data-for-account.html>`_
:param str address: The account ID to look up a data item from.
:param str key: The name of the key for the data item in question.
:return: The value of the data field for the given account and data key.
:rtype: dict
"""
endpoint = '/accounts/{account_id}/data/{data_key}'.format(
account_id=address, data_key=key)
return self.query(endpoint) | python | def account_data(self, address, key):
"""This endpoint represents a single data associated with a given
account.
`GET /accounts/{account}/data/{key}
<https://www.stellar.org/developers/horizon/reference/endpoints/data-for-account.html>`_
:param str address: The account ID to look up a data item from.
:param str key: The name of the key for the data item in question.
:return: The value of the data field for the given account and data key.
:rtype: dict
"""
endpoint = '/accounts/{account_id}/data/{data_key}'.format(
account_id=address, data_key=key)
return self.query(endpoint) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.account_effects | def account_effects(self, address, cursor=None, order='asc', limit=10, sse=False):
"""This endpoint represents all effects that changed a given account.
`GET /accounts/{account}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-account.html>`_
:param str address: The account ID to look up effects for.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
:return: The list of effects in a JSON response.
:rtype: dict
"""
endpoint = '/accounts/{account_id}/effects'.format(account_id=address)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params, sse) | python | def account_effects(self, address, cursor=None, order='asc', limit=10, sse=False):
"""This endpoint represents all effects that changed a given account.
`GET /accounts/{account}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-account.html>`_
:param str address: The account ID to look up effects for.
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
:return: The list of effects in a JSON response.
:rtype: dict
"""
endpoint = '/accounts/{account_id}/effects'.format(account_id=address)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params, sse) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.assets | def assets(self, asset_code=None, asset_issuer=None, cursor=None, order='asc', limit=10):
"""This endpoint represents all assets. It will give you all the assets
in the system along with various statistics about each.
See the documentation below for details on query parameters that are
available.
`GET /assets{?asset_code,asset_issuer,cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/assets-all.html>`_
:param str asset_code: Code of the Asset to filter by.
:param str asset_issuer: Issuer of the Asset to filter by.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc",
ordered by asset_code then by asset_issuer.
:param int limit: Maximum number of records to return.
:return: A list of all valid payment operations
:rtype: dict
"""
endpoint = '/assets'
params = self.__query_params(asset_code=asset_code, asset_issuer=asset_issuer, cursor=cursor, order=order,
limit=limit)
return self.query(endpoint, params) | python | def assets(self, asset_code=None, asset_issuer=None, cursor=None, order='asc', limit=10):
"""This endpoint represents all assets. It will give you all the assets
in the system along with various statistics about each.
See the documentation below for details on query parameters that are
available.
`GET /assets{?asset_code,asset_issuer,cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/assets-all.html>`_
:param str asset_code: Code of the Asset to filter by.
:param str asset_issuer: Issuer of the Asset to filter by.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc",
ordered by asset_code then by asset_issuer.
:param int limit: Maximum number of records to return.
:return: A list of all valid payment operations
:rtype: dict
"""
endpoint = '/assets'
params = self.__query_params(asset_code=asset_code, asset_issuer=asset_issuer, cursor=cursor, order=order,
limit=limit)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.transaction | def transaction(self, tx_hash):
"""The transaction details endpoint provides information on a single
transaction.
`GET /transactions/{hash}
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-single.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:return: A single transaction's details.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}'.format(tx_hash=tx_hash)
return self.query(endpoint) | python | def transaction(self, tx_hash):
"""The transaction details endpoint provides information on a single
transaction.
`GET /transactions/{hash}
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-single.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:return: A single transaction's details.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}'.format(tx_hash=tx_hash)
return self.query(endpoint) | [
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`GET /transactions/{hash}
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.transaction_operations | def transaction_operations(self, tx_hash, cursor=None, order='asc', include_failed=False, limit=10):
"""This endpoint represents all operations that are part of a given
transaction.
`GET /transactions/{hash}/operations{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-for-transaction.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include operations of failed transactions in results.
:return: A single transaction's operations.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}/operations'.format(tx_hash=tx_hash)
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params) | python | def transaction_operations(self, tx_hash, cursor=None, order='asc', include_failed=False, limit=10):
"""This endpoint represents all operations that are part of a given
transaction.
`GET /transactions/{hash}/operations{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-for-transaction.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include operations of failed transactions in results.
:return: A single transaction's operations.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}/operations'.format(tx_hash=tx_hash)
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.transaction_effects | def transaction_effects(self, tx_hash, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred as a result of a
given transaction.
`GET /transactions/{hash}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-transaction.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A single transaction's effects.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}/effects'.format(tx_hash=tx_hash)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | python | def transaction_effects(self, tx_hash, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred as a result of a
given transaction.
`GET /transactions/{hash}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-transaction.html>`_
:param str tx_hash: The hex-encoded transaction hash.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A single transaction's effects.
:rtype: dict
"""
endpoint = '/transactions/{tx_hash}/effects'.format(tx_hash=tx_hash)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.order_book | def order_book(self, selling_asset_code, buying_asset_code, selling_asset_issuer=None, buying_asset_issuer=None,
limit=10):
"""Return, for each orderbook, a summary of the orderbook and the bids
and asks associated with that orderbook.
See the external docs below for information on the arguments required.
`GET /order_book
<https://www.stellar.org/developers/horizon/reference/endpoints/orderbook-details.html>`_
:param str selling_asset_code: Code of the Asset being sold.
:param str buying_asset_code: Type of the Asset being bought.
:param str selling_asset_issuer: Account ID of the issuer of the Asset being sold,
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:param str buying_asset_issuer: Account ID of the issuer of the Asset being bought,
if it is a native asset, let it be `None`.
:param int limit: Limit the number of items returned.
:return: A list of orderbook summaries as a JSON object.
:rtype: dict
"""
selling_asset = Asset(selling_asset_code, selling_asset_issuer)
buying_asset = Asset(buying_asset_code, buying_asset_issuer)
asset_params = {
'selling_asset_type': selling_asset.type,
'selling_asset_code': None if selling_asset.is_native() else selling_asset.code,
'selling_asset_issuer': selling_asset.issuer,
'buying_asset_type': buying_asset.type,
'buying_asset_code': None if buying_asset.is_native() else buying_asset.code,
'buying_asset_issuer': buying_asset.issuer,
}
endpoint = '/order_book'
params = self.__query_params(limit=limit, **asset_params)
return self.query(endpoint, params) | python | def order_book(self, selling_asset_code, buying_asset_code, selling_asset_issuer=None, buying_asset_issuer=None,
limit=10):
"""Return, for each orderbook, a summary of the orderbook and the bids
and asks associated with that orderbook.
See the external docs below for information on the arguments required.
`GET /order_book
<https://www.stellar.org/developers/horizon/reference/endpoints/orderbook-details.html>`_
:param str selling_asset_code: Code of the Asset being sold.
:param str buying_asset_code: Type of the Asset being bought.
:param str selling_asset_issuer: Account ID of the issuer of the Asset being sold,
if it is a native asset, let it be `None`.
:param str buying_asset_issuer: Account ID of the issuer of the Asset being bought,
if it is a native asset, let it be `None`.
:param int limit: Limit the number of items returned.
:return: A list of orderbook summaries as a JSON object.
:rtype: dict
"""
selling_asset = Asset(selling_asset_code, selling_asset_issuer)
buying_asset = Asset(buying_asset_code, buying_asset_issuer)
asset_params = {
'selling_asset_type': selling_asset.type,
'selling_asset_code': None if selling_asset.is_native() else selling_asset.code,
'selling_asset_issuer': selling_asset.issuer,
'buying_asset_type': buying_asset.type,
'buying_asset_code': None if buying_asset.is_native() else buying_asset.code,
'buying_asset_issuer': buying_asset.issuer,
}
endpoint = '/order_book'
params = self.__query_params(limit=limit, **asset_params)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.ledger | def ledger(self, ledger_id):
"""The ledger details endpoint provides information on a single ledger.
`GET /ledgers/{sequence}
<https://www.stellar.org/developers/horizon/reference/endpoints/ledgers-single.html>`_
:param int ledger_id: The id of the ledger to look up.
:return: The details of a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}'.format(ledger_id=ledger_id)
return self.query(endpoint) | python | def ledger(self, ledger_id):
"""The ledger details endpoint provides information on a single ledger.
`GET /ledgers/{sequence}
<https://www.stellar.org/developers/horizon/reference/endpoints/ledgers-single.html>`_
:param int ledger_id: The id of the ledger to look up.
:return: The details of a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}'.format(ledger_id=ledger_id)
return self.query(endpoint) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.ledger_effects | def ledger_effects(self, ledger_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred in the given
ledger.
`GET /ledgers/{id}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-ledger.html>`_
:param int ledger_id: The id of the ledger to look up.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: The effects for a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}/effects'.format(ledger_id=ledger_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | python | def ledger_effects(self, ledger_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred in the given
ledger.
`GET /ledgers/{id}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-ledger.html>`_
:param int ledger_id: The id of the ledger to look up.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: The effects for a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}/effects'.format(ledger_id=ledger_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.ledger_transactions | def ledger_transactions(self, ledger_id, cursor=None, order='asc', include_failed=False, limit=10):
"""This endpoint represents all transactions in a given ledger.
`GET /ledgers/{id}/transactions{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-for-ledger.html>`_
:param int ledger_id: The id of the ledger to look up.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include failed transactions in results.
:return: The transactions contained in a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}/transactions'.format(
ledger_id=ledger_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params) | python | def ledger_transactions(self, ledger_id, cursor=None, order='asc', include_failed=False, limit=10):
"""This endpoint represents all transactions in a given ledger.
`GET /ledgers/{id}/transactions{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/transactions-for-ledger.html>`_
:param int ledger_id: The id of the ledger to look up.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include failed transactions in results.
:return: The transactions contained in a single ledger.
:rtype: dict
"""
endpoint = '/ledgers/{ledger_id}/transactions'.format(
ledger_id=ledger_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.effects | def effects(self, cursor=None, order='asc', limit=10, sse=False):
"""This endpoint represents all effects.
`GET /effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-all.html>`_
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
:return: A list of all effects.
:rtype: dict
"""
endpoint = '/effects'
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params, sse) | python | def effects(self, cursor=None, order='asc', limit=10, sse=False):
"""This endpoint represents all effects.
`GET /effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-all.html>`_
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool sse: Use server side events for streaming responses.
:return: A list of all effects.
:rtype: dict
"""
endpoint = '/effects'
params = self.__query_params(cursor=cursor, order=order, limit=limit)
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.operations | def operations(self, cursor=None, order='asc', limit=10, include_failed=False, sse=False):
"""This endpoint represents all operations that are part of validated
transactions.
`GET /operations{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-all.html>`_
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include operations of failed transactions in results.
:param bool sse: Use server side events for streaming responses.
:return: A list of all operations.
:rtype: dict
"""
endpoint = '/operations'
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params, sse) | python | def operations(self, cursor=None, order='asc', limit=10, include_failed=False, sse=False):
"""This endpoint represents all operations that are part of validated
transactions.
`GET /operations{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-all.html>`_
:param cursor: A paging token, specifying where to start returning records from.
When streaming this can be set to "now" to stream object created since your request time.
:type cursor: int, str
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param bool include_failed: Set to `True` to include operations of failed transactions in results.
:param bool sse: Use server side events for streaming responses.
:return: A list of all operations.
:rtype: dict
"""
endpoint = '/operations'
params = self.__query_params(cursor=cursor, order=order, limit=limit, include_failed=include_failed)
return self.query(endpoint, params, sse) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.operation | def operation(self, op_id):
"""The operation details endpoint provides information on a single
operation.
`GET /operations/{id}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-single.html>`_
:param id op_id: The operation ID to get details on.
:return: Details on a single operation.
:rtype: dict
"""
endpoint = '/operations/{op_id}'.format(op_id=op_id)
return self.query(endpoint) | python | def operation(self, op_id):
"""The operation details endpoint provides information on a single
operation.
`GET /operations/{id}
<https://www.stellar.org/developers/horizon/reference/endpoints/operations-single.html>`_
:param id op_id: The operation ID to get details on.
:return: Details on a single operation.
:rtype: dict
"""
endpoint = '/operations/{op_id}'.format(op_id=op_id)
return self.query(endpoint) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.operation_effects | def operation_effects(self, op_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred as a result of a
given operation.
`GET /operations/{id}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-operation.html>`_
:param int op_id: The operation ID to get effects on.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of effects on the given operation.
:rtype: dict
"""
endpoint = '/operations/{op_id}/effects'.format(op_id=op_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | python | def operation_effects(self, op_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all effects that occurred as a result of a
given operation.
`GET /operations/{id}/effects{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/effects-for-operation.html>`_
:param int op_id: The operation ID to get effects on.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of effects on the given operation.
:rtype: dict
"""
endpoint = '/operations/{op_id}/effects'.format(op_id=op_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.paths | def paths(self, destination_account, destination_amount, source_account, destination_asset_code,
destination_asset_issuer=None):
"""Load a list of assets available to the source account id and find
any payment paths from those source assets to the desired
destination asset.
See the below docs for more information on required and optional
parameters for further specifying your search.
`GET /paths
<https://www.stellar.org/developers/horizon/reference/endpoints/path-finding.html>`_
:param str destination_account: The destination account that any returned path should use.
:param str destination_amount: The amount, denominated in the destination asset,
that any returned path should be able to satisfy.
:param str source_account: The sender's account id. Any returned path must use a source that the sender can hold.
:param str destination_asset_code: The asset code for the destination.
:param destination_asset_issuer: The asset issuer for the destination, if it is a native asset, let it be `None`.
:type destination_asset_issuer: str, None
:return: A list of paths that can be used to complete a payment based
on a given query.
:rtype: dict
"""
destination_asset = Asset(destination_asset_code, destination_asset_issuer)
destination_asset_params = {
'destination_asset_type': destination_asset.type,
'destination_asset_code': None if destination_asset.is_native() else destination_asset.code,
'destination_asset_issuer': destination_asset.issuer
}
endpoint = '/paths'
params = self.__query_params(destination_account=destination_account,
source_account=source_account,
destination_amount=destination_amount,
**destination_asset_params
)
return self.query(endpoint, params) | python | def paths(self, destination_account, destination_amount, source_account, destination_asset_code,
destination_asset_issuer=None):
"""Load a list of assets available to the source account id and find
any payment paths from those source assets to the desired
destination asset.
See the below docs for more information on required and optional
parameters for further specifying your search.
`GET /paths
<https://www.stellar.org/developers/horizon/reference/endpoints/path-finding.html>`_
:param str destination_account: The destination account that any returned path should use.
:param str destination_amount: The amount, denominated in the destination asset,
that any returned path should be able to satisfy.
:param str source_account: The sender's account id. Any returned path must use a source that the sender can hold.
:param str destination_asset_code: The asset code for the destination.
:param destination_asset_issuer: The asset issuer for the destination, if it is a native asset, let it be `None`.
:type destination_asset_issuer: str, None
:return: A list of paths that can be used to complete a payment based
on a given query.
:rtype: dict
"""
destination_asset = Asset(destination_asset_code, destination_asset_issuer)
destination_asset_params = {
'destination_asset_type': destination_asset.type,
'destination_asset_code': None if destination_asset.is_native() else destination_asset.code,
'destination_asset_issuer': destination_asset.issuer
}
endpoint = '/paths'
params = self.__query_params(destination_account=destination_account,
source_account=source_account,
destination_amount=destination_amount,
**destination_asset_params
)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.trades | def trades(self, base_asset_code=None, counter_asset_code=None, base_asset_issuer=None, counter_asset_issuer=None,
offer_id=None, cursor=None, order='asc', limit=10):
"""Load a list of trades, optionally filtered by an orderbook.
See the below docs for more information on required and optional
parameters for further specifying your search.
`GET /trades
<https://www.stellar.org/developers/horizon/reference/endpoints/trades.html>`_
:param str base_asset_code: Code of base asset.
:param str base_asset_issuer: Issuer of base asset, if it is a native asset, let it be `None`.
:param str counter_asset_code: Code of counter asset.
:param str counter_asset_issuer: Issuer of counter asset, if it is a native asset, let it be `None`.
:param int offer_id: Filter for by a specific offer id.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of trades filtered by a given query.
:rtype: dict
"""
base_asset = Asset(base_asset_code, base_asset_issuer)
counter_asset = Asset(counter_asset_code, counter_asset_issuer)
asset_params = {
'base_asset_type': base_asset.type,
'base_asset_code': None if base_asset.is_native() else base_asset.code,
'base_asset_issuer': base_asset.issuer,
'counter_asset_type': counter_asset.type,
'counter_asset_code': None if counter_asset.is_native() else counter_asset.code,
'counter_asset_issuer': counter_asset.issuer
}
endpoint = '/trades'
params = self.__query_params(offer_id=offer_id, cursor=cursor, order=order, limit=limit, **asset_params)
return self.query(endpoint, params) | python | def trades(self, base_asset_code=None, counter_asset_code=None, base_asset_issuer=None, counter_asset_issuer=None,
offer_id=None, cursor=None, order='asc', limit=10):
"""Load a list of trades, optionally filtered by an orderbook.
See the below docs for more information on required and optional
parameters for further specifying your search.
`GET /trades
<https://www.stellar.org/developers/horizon/reference/endpoints/trades.html>`_
:param str base_asset_code: Code of base asset.
:param str base_asset_issuer: Issuer of base asset, if it is a native asset, let it be `None`.
:param str counter_asset_code: Code of counter asset.
:param str counter_asset_issuer: Issuer of counter asset, if it is a native asset, let it be `None`.
:param int offer_id: Filter for by a specific offer id.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of trades filtered by a given query.
:rtype: dict
"""
base_asset = Asset(base_asset_code, base_asset_issuer)
counter_asset = Asset(counter_asset_code, counter_asset_issuer)
asset_params = {
'base_asset_type': base_asset.type,
'base_asset_code': None if base_asset.is_native() else base_asset.code,
'base_asset_issuer': base_asset.issuer,
'counter_asset_type': counter_asset.type,
'counter_asset_code': None if counter_asset.is_native() else counter_asset.code,
'counter_asset_issuer': counter_asset.issuer
}
endpoint = '/trades'
params = self.__query_params(offer_id=offer_id, cursor=cursor, order=order, limit=limit, **asset_params)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.trade_aggregations | def trade_aggregations(self, resolution, base_asset_code, counter_asset_code,
base_asset_issuer=None, counter_asset_issuer=None, start_time=None,
end_time=None, order='asc', limit=10, offset=0):
"""Load a list of aggregated historical trade data, optionally filtered
by an orderbook.
`GET /trade_aggregations
<https://www.stellar.org/developers/horizon/reference/endpoints/trade_aggregations.html>`_
:param int start_time: Lower time boundary represented as millis since epoch.
:param int end_time: Upper time boundary represented as millis since epoch.
:param int resolution: Segment duration as millis since epoch. Supported values
are 1 minute (60000), 5 minutes (300000), 15 minutes (900000), 1 hour (3600000),
1 day (86400000) and 1 week (604800000).
:param str base_asset_code: Code of base asset.
:param str base_asset_issuer: Issuer of base asset, if it is a native asset, let it be `None`.
:param str counter_asset_code: Code of counter asset.
:param str counter_asset_issuer: Issuer of counter asset, if it is a native asset, let it be `None`.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param int offset: segments can be offset using this parameter.
Expressed in milliseconds. *Can only be used if the resolution is greater than 1 hour.
Value must be in whole hours, less than the provided resolution, and less than 24 hours.
:return: A list of collected trade aggregations.
:rtype: dict
"""
allowed_resolutions = (60000, 300000, 900000, 3600000, 86400000, 604800000)
if resolution not in allowed_resolutions:
raise NotValidParamError("resolution is invalid")
if offset > resolution or offset >= 24 * 3600000 or offset % 3600000 != 0:
raise NotValidParamError("offset is invalid")
base_asset = Asset(base_asset_code, base_asset_issuer)
counter_asset = Asset(counter_asset_code, counter_asset_issuer)
asset_params = {
'base_asset_type': base_asset.type,
'base_asset_code': None if base_asset.is_native() else base_asset.code,
'base_asset_issuer': base_asset.issuer,
'counter_asset_type': counter_asset.type,
'counter_asset_code': None if counter_asset.is_native() else counter_asset.code,
'counter_asset_issuer': counter_asset.issuer
}
endpoint = '/trade_aggregations'
params = self.__query_params(start_time=start_time, end_time=end_time, resolution=resolution, order=order,
limit=limit, offset=offset, **asset_params)
return self.query(endpoint, params) | python | def trade_aggregations(self, resolution, base_asset_code, counter_asset_code,
base_asset_issuer=None, counter_asset_issuer=None, start_time=None,
end_time=None, order='asc', limit=10, offset=0):
"""Load a list of aggregated historical trade data, optionally filtered
by an orderbook.
`GET /trade_aggregations
<https://www.stellar.org/developers/horizon/reference/endpoints/trade_aggregations.html>`_
:param int start_time: Lower time boundary represented as millis since epoch.
:param int end_time: Upper time boundary represented as millis since epoch.
:param int resolution: Segment duration as millis since epoch. Supported values
are 1 minute (60000), 5 minutes (300000), 15 minutes (900000), 1 hour (3600000),
1 day (86400000) and 1 week (604800000).
:param str base_asset_code: Code of base asset.
:param str base_asset_issuer: Issuer of base asset, if it is a native asset, let it be `None`.
:param str counter_asset_code: Code of counter asset.
:param str counter_asset_issuer: Issuer of counter asset, if it is a native asset, let it be `None`.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:param int offset: segments can be offset using this parameter.
Expressed in milliseconds. *Can only be used if the resolution is greater than 1 hour.
Value must be in whole hours, less than the provided resolution, and less than 24 hours.
:return: A list of collected trade aggregations.
:rtype: dict
"""
allowed_resolutions = (60000, 300000, 900000, 3600000, 86400000, 604800000)
if resolution not in allowed_resolutions:
raise NotValidParamError("resolution is invalid")
if offset > resolution or offset >= 24 * 3600000 or offset % 3600000 != 0:
raise NotValidParamError("offset is invalid")
base_asset = Asset(base_asset_code, base_asset_issuer)
counter_asset = Asset(counter_asset_code, counter_asset_issuer)
asset_params = {
'base_asset_type': base_asset.type,
'base_asset_code': None if base_asset.is_native() else base_asset.code,
'base_asset_issuer': base_asset.issuer,
'counter_asset_type': counter_asset.type,
'counter_asset_code': None if counter_asset.is_native() else counter_asset.code,
'counter_asset_issuer': counter_asset.issuer
}
endpoint = '/trade_aggregations'
params = self.__query_params(start_time=start_time, end_time=end_time, resolution=resolution, order=order,
limit=limit, offset=offset, **asset_params)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/horizon.py | Horizon.offer_trades | def offer_trades(self, offer_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all trades for a given offer.
`GET /offers/{offer_id}/trades{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/trades-for-offer.html>`_
:param int offer_id: The offer ID to get trades on.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of effects on the given operation.
:rtype: dict
"""
endpoint = '/offers/{offer_id}/trades'.format(offer_id=offer_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | python | def offer_trades(self, offer_id, cursor=None, order='asc', limit=10):
"""This endpoint represents all trades for a given offer.
`GET /offers/{offer_id}/trades{?cursor,limit,order}
<https://www.stellar.org/developers/horizon/reference/endpoints/trades-for-offer.html>`_
:param int offer_id: The offer ID to get trades on.
:param int cursor: A paging token, specifying where to start returning records from.
:param str order: The order in which to return rows, "asc" or "desc".
:param int limit: Maximum number of records to return.
:return: A list of effects on the given operation.
:rtype: dict
"""
endpoint = '/offers/{offer_id}/trades'.format(offer_id=offer_id)
params = self.__query_params(cursor=cursor, order=order, limit=limit)
return self.query(endpoint, params) | [
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StellarCN/py-stellar-base | stellar_base/transaction_envelope.py | TransactionEnvelope.sign | def sign(self, keypair):
"""Sign this transaction envelope with a given keypair.
Note that the signature must not already be in this instance's list of
signatures.
:param keypair: The keypair to use for signing this transaction
envelope.
:type keypair: :class:`Keypair <stellar_base.keypair.Keypair>`
:raises: :exc:`SignatureExistError
<stellar_base.utils.SignatureExistError>`
"""
assert isinstance(keypair, Keypair)
tx_hash = self.hash_meta()
sig = keypair.sign_decorated(tx_hash)
sig_dict = [signature.__dict__ for signature in self.signatures]
if sig.__dict__ in sig_dict:
raise SignatureExistError('The keypair has already signed')
else:
self.signatures.append(sig) | python | def sign(self, keypair):
"""Sign this transaction envelope with a given keypair.
Note that the signature must not already be in this instance's list of
signatures.
:param keypair: The keypair to use for signing this transaction
envelope.
:type keypair: :class:`Keypair <stellar_base.keypair.Keypair>`
:raises: :exc:`SignatureExistError
<stellar_base.utils.SignatureExistError>`
"""
assert isinstance(keypair, Keypair)
tx_hash = self.hash_meta()
sig = keypair.sign_decorated(tx_hash)
sig_dict = [signature.__dict__ for signature in self.signatures]
if sig.__dict__ in sig_dict:
raise SignatureExistError('The keypair has already signed')
else:
self.signatures.append(sig) | [
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StellarCN/py-stellar-base | stellar_base/transaction_envelope.py | TransactionEnvelope.signature_base | def signature_base(self):
"""Get the signature base of this transaction envelope.
Return the "signature base" of this transaction, which is the value
that, when hashed, should be signed to create a signature that
validators on the Stellar Network will accept.
It is composed of a 4 prefix bytes followed by the xdr-encoded form of
this transaction.
:return: The signature base of this transaction envelope.
"""
network_id = self.network_id
tx_type = Xdr.StellarXDRPacker()
tx_type.pack_EnvelopeType(Xdr.const.ENVELOPE_TYPE_TX)
tx_type = tx_type.get_buffer()
tx = Xdr.StellarXDRPacker()
tx.pack_Transaction(self.tx.to_xdr_object())
tx = tx.get_buffer()
return network_id + tx_type + tx | python | def signature_base(self):
"""Get the signature base of this transaction envelope.
Return the "signature base" of this transaction, which is the value
that, when hashed, should be signed to create a signature that
validators on the Stellar Network will accept.
It is composed of a 4 prefix bytes followed by the xdr-encoded form of
this transaction.
:return: The signature base of this transaction envelope.
"""
network_id = self.network_id
tx_type = Xdr.StellarXDRPacker()
tx_type.pack_EnvelopeType(Xdr.const.ENVELOPE_TYPE_TX)
tx_type = tx_type.get_buffer()
tx = Xdr.StellarXDRPacker()
tx.pack_Transaction(self.tx.to_xdr_object())
tx = tx.get_buffer()
return network_id + tx_type + tx | [
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StellarCN/py-stellar-base | stellar_base/federation.py | get_federation_service | def get_federation_service(domain, allow_http=False):
"""Retrieve the FEDERATION_SERVER config from a domain's stellar.toml.
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return str: The FEDERATION_SERVER url.
"""
st = get_stellar_toml(domain, allow_http)
if not st:
return None
return st.get('FEDERATION_SERVER') | python | def get_federation_service(domain, allow_http=False):
"""Retrieve the FEDERATION_SERVER config from a domain's stellar.toml.
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return str: The FEDERATION_SERVER url.
"""
st = get_stellar_toml(domain, allow_http)
if not st:
return None
return st.get('FEDERATION_SERVER') | [
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StellarCN/py-stellar-base | stellar_base/federation.py | get_auth_server | def get_auth_server(domain, allow_http=False):
"""Retrieve the AUTH_SERVER config from a domain's stellar.toml.
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return str: The AUTH_SERVER url.
"""
st = get_stellar_toml(domain, allow_http)
if not st:
return None
return st.get('AUTH_SERVER') | python | def get_auth_server(domain, allow_http=False):
"""Retrieve the AUTH_SERVER config from a domain's stellar.toml.
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return str: The AUTH_SERVER url.
"""
st = get_stellar_toml(domain, allow_http)
if not st:
return None
return st.get('AUTH_SERVER') | [
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] | cce2e782064fb3955c85e1696e630d67b1010848 | https://github.com/StellarCN/py-stellar-base/blob/cce2e782064fb3955c85e1696e630d67b1010848/stellar_base/federation.py#L91-L103 | train | 229,293 |
StellarCN/py-stellar-base | stellar_base/federation.py | get_stellar_toml | def get_stellar_toml(domain, allow_http=False):
"""Retrieve the stellar.toml file from a given domain.
Retrieve the stellar.toml file for information about interacting with
Stellar's federation protocol for a given Stellar Anchor (specified by a
domain).
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return: The stellar.toml file as a an object via :func:`toml.loads`.
"""
toml_link = '/.well-known/stellar.toml'
if allow_http:
protocol = 'http://'
else:
protocol = 'https://'
url_list = ['', 'www.', 'stellar.']
url_list = [protocol + url + domain + toml_link for url in url_list]
for url in url_list:
r = requests.get(url)
if r.status_code == 200:
return toml.loads(r.text)
return None | python | def get_stellar_toml(domain, allow_http=False):
"""Retrieve the stellar.toml file from a given domain.
Retrieve the stellar.toml file for information about interacting with
Stellar's federation protocol for a given Stellar Anchor (specified by a
domain).
:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
HTTP vs HTTPS. Note it is recommend that you *always* use HTTPS.
:return: The stellar.toml file as a an object via :func:`toml.loads`.
"""
toml_link = '/.well-known/stellar.toml'
if allow_http:
protocol = 'http://'
else:
protocol = 'https://'
url_list = ['', 'www.', 'stellar.']
url_list = [protocol + url + domain + toml_link for url in url_list]
for url in url_list:
r = requests.get(url)
if r.status_code == 200:
return toml.loads(r.text)
return None | [
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:param str domain: The domain the .toml file is hosted at.
:param bool allow_http: Specifies whether the request should go over plain
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StellarCN/py-stellar-base | stellar_base/keypair.py | Keypair.account_xdr_object | def account_xdr_object(self):
"""Create PublicKey XDR object via public key bytes.
:return: Serialized XDR of PublicKey type.
"""
return Xdr.types.PublicKey(Xdr.const.KEY_TYPE_ED25519,
self.verifying_key.to_bytes()) | python | def account_xdr_object(self):
"""Create PublicKey XDR object via public key bytes.
:return: Serialized XDR of PublicKey type.
"""
return Xdr.types.PublicKey(Xdr.const.KEY_TYPE_ED25519,
self.verifying_key.to_bytes()) | [
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] | cce2e782064fb3955c85e1696e630d67b1010848 | https://github.com/StellarCN/py-stellar-base/blob/cce2e782064fb3955c85e1696e630d67b1010848/stellar_base/keypair.py#L154-L160 | train | 229,295 |
StellarCN/py-stellar-base | stellar_base/keypair.py | Keypair.xdr | def xdr(self):
"""Generate base64 encoded XDR PublicKey object.
Return a base64 encoded PublicKey XDR object, for sending over the wire
when interacting with stellar.
:return: The base64 encoded PublicKey XDR structure.
"""
kp = Xdr.StellarXDRPacker()
kp.pack_PublicKey(self.account_xdr_object())
return base64.b64encode(kp.get_buffer()) | python | def xdr(self):
"""Generate base64 encoded XDR PublicKey object.
Return a base64 encoded PublicKey XDR object, for sending over the wire
when interacting with stellar.
:return: The base64 encoded PublicKey XDR structure.
"""
kp = Xdr.StellarXDRPacker()
kp.pack_PublicKey(self.account_xdr_object())
return base64.b64encode(kp.get_buffer()) | [
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Return a base64 encoded PublicKey XDR object, for sending over the wire
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:return: The base64 encoded PublicKey XDR structure. | [
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] | cce2e782064fb3955c85e1696e630d67b1010848 | https://github.com/StellarCN/py-stellar-base/blob/cce2e782064fb3955c85e1696e630d67b1010848/stellar_base/keypair.py#L162-L172 | train | 229,296 |
StellarCN/py-stellar-base | stellar_base/keypair.py | Keypair.verify | def verify(self, data, signature):
"""Verify the signature of a sequence of bytes.
Verify the signature of a sequence of bytes using the verifying
(public) key and the data that was originally signed, otherwise throws
an exception.
:param bytes data: A sequence of bytes that were previously signed by
the private key associated with this verifying key.
:param bytes signature: A sequence of bytes that comprised the
signature for the corresponding data.
"""
try:
return self.verifying_key.verify(signature, data)
except ed25519.BadSignatureError:
raise BadSignatureError("Signature verification failed.") | python | def verify(self, data, signature):
"""Verify the signature of a sequence of bytes.
Verify the signature of a sequence of bytes using the verifying
(public) key and the data that was originally signed, otherwise throws
an exception.
:param bytes data: A sequence of bytes that were previously signed by
the private key associated with this verifying key.
:param bytes signature: A sequence of bytes that comprised the
signature for the corresponding data.
"""
try:
return self.verifying_key.verify(signature, data)
except ed25519.BadSignatureError:
raise BadSignatureError("Signature verification failed.") | [
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Verify the signature of a sequence of bytes using the verifying
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an exception.
:param bytes data: A sequence of bytes that were previously signed by
the private key associated with this verifying key.
:param bytes signature: A sequence of bytes that comprised the
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] | cce2e782064fb3955c85e1696e630d67b1010848 | https://github.com/StellarCN/py-stellar-base/blob/cce2e782064fb3955c85e1696e630d67b1010848/stellar_base/keypair.py#L228-L243 | train | 229,297 |
StellarCN/py-stellar-base | stellar_base/keypair.py | Keypair.sign_decorated | def sign_decorated(self, data):
"""Sign a bytes-like object and return the decorated signature.
Sign a bytes-like object by signing the data using the signing
(private) key, and return a decorated signature, which includes the
last four bytes of the public key as a signature hint to go along with
the signature as an XDR DecoratedSignature object.
:param bytes data: A sequence of bytes to sign, typically a
transaction.
"""
signature = self.sign(data)
hint = self.signature_hint()
return Xdr.types.DecoratedSignature(hint, signature) | python | def sign_decorated(self, data):
"""Sign a bytes-like object and return the decorated signature.
Sign a bytes-like object by signing the data using the signing
(private) key, and return a decorated signature, which includes the
last four bytes of the public key as a signature hint to go along with
the signature as an XDR DecoratedSignature object.
:param bytes data: A sequence of bytes to sign, typically a
transaction.
"""
signature = self.sign(data)
hint = self.signature_hint()
return Xdr.types.DecoratedSignature(hint, signature) | [
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last four bytes of the public key as a signature hint to go along with
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] | cce2e782064fb3955c85e1696e630d67b1010848 | https://github.com/StellarCN/py-stellar-base/blob/cce2e782064fb3955c85e1696e630d67b1010848/stellar_base/keypair.py#L245-L259 | train | 229,298 |
StellarCN/py-stellar-base | stellar_base/utils.py | bytes_from_decode_data | def bytes_from_decode_data(s):
"""copy from base64._bytes_from_decode_data
"""
if isinstance(s, (str, unicode)):
try:
return s.encode('ascii')
except UnicodeEncodeError:
raise NotValidParamError(
'String argument should contain only ASCII characters')
if isinstance(s, bytes_types):
return s
try:
return memoryview(s).tobytes()
except TypeError:
raise suppress_context(
TypeError(
'Argument should be a bytes-like object or ASCII string, not '
'{!r}'.format(s.__class__.__name__))) | python | def bytes_from_decode_data(s):
"""copy from base64._bytes_from_decode_data
"""
if isinstance(s, (str, unicode)):
try:
return s.encode('ascii')
except UnicodeEncodeError:
raise NotValidParamError(
'String argument should contain only ASCII characters')
if isinstance(s, bytes_types):
return s
try:
return memoryview(s).tobytes()
except TypeError:
raise suppress_context(
TypeError(
'Argument should be a bytes-like object or ASCII string, not '
'{!r}'.format(s.__class__.__name__))) | [
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