add paper trading back testing

bidder.py

@@ -0,0 +1,33 @@
+import numpy as np
+
+#test
+import streamlit as st
+
+def buy_and_hold(init_val, symbol_closes):
+    shares = init_val/symbol_closes[0]
+    trade_val = shares*symbol_closes[-1]
+    return trade_val
+
+def buy_rule(init_value, transact_dates, transact_percents, dates, symbol_closes):
+    close_values = [symbol_closes[dates.index(transact_date)] for transact_date in transact_dates]
+    wallet = init_value
+    shares = 0.0
+    for close_value, transact_percent in zip(close_values, transact_percents):
+        is_buy = transact_percent > 0
+        if is_buy:
+            transact_val = wallet*transact_percent/100
+            transact_shares = transact_val/close_value
+            wallet -= transact_val
+            shares += transact_shares
+
+            # st.write(f"buy: R{transact_val} = {transact_shares}shares. Remaining: R{wallet} and {shares}shares")
+        else:
+            transact_shares = shares*-transact_percent/100
+            transact_val = transact_shares*close_value
+            shares -= transact_shares
+            wallet += transact_val
+
+            # st.write(f"sell: R{transact_val} = {transact_shares}shares. Remaining: R{wallet} and {shares}shares")
+
+    share_value = shares*symbol_closes[-1]
+    return wallet + share_value

calculator.py

@@ -2,6 +2,8 @@ import numpy as np
 import scipy as sp
 from datetime import datetime
 
+# test
+import streamlit as st
 
 def linear_regression_line(dates, y_list):
     if not any(dates):
@@ -64,4 +66,15 @@ def normalize(data_list, high=1.0, low=-1.0):
     new_delta = high - low
 
     data = ((data - min_val) * new_delta / delta) + low
-    return data
+    return data
+
+def intercepts(data1, data2):
+    intercept_indices = []
+    prev_data1_is_above = True
+    for index, (data1_v, data2_v) in enumerate(zip(data1, data2)):
+        data1_is_above = data1_v - data2_v >= 0.0
+        if index is not 0 and (not any(intercept_indices) or index is not intercept_indices[-1] + 1) and prev_data1_is_above is not data1_is_above:
+            intercept_indices.append(index)
+        prev_data1_is_above = data1_is_above
+
+    return intercept_indices

page_symbol_details.py

@@ -8,6 +8,7 @@ import calculator
 import data_retriever
 import trends
 import ui
+import bidder
 
 
 def run():
@@ -52,58 +53,94 @@ def run():
     # symbol candlestick graph
     candleFigure = make_subplots(rows=1, cols=1)
     ui.create_candlestick(candleFigure, dates, symbol_prices, symbol, 'Price')
-    # # indicators
-    # indicator_datasets = indicators.create_indicators(dates, symbol_prices)
-    # ui.create_indicators(fig1, indicator_datasets)
-    # trend lines
-    # sigma_multiplier = st.slider(label='min trend deviation', min_value=1.0, max_value=10.0, value=3.0, step=0.1)
-    # sigma_dates, sigma_values = trends.trend_line(symbol_prices_backtest.index, np.array(symbol_prices_backtest['Close']), min_trend_size=21, sigma_multiplier=sigma_multiplier)
-    # ui.create_markers(candleFigure, sigma_dates, sigma_values, f"{sigma_multiplier} Deviations from regression line", 'Deviations')
-
-    # # vwap
-    # vwap = trends.vwap(symbol_prices_backtest)
-    # ui.create_line(candleFigure, backtest_dates, vwap, "Volume Weighted Average Price (VWAP)", "VWAP")
-
-    # # bollinger bands
-    # bollinger_dates, bollinger_low, bollinger_high = trends.bollinger_bands(backtest_dates, symbol_prices_backtest)
-    # ui.create_fill_area(candleFigure, bollinger_dates, bollinger_low, bollinger_high, "Bollinger Bands")
-
-    normalization_factor = 0.20*(max(symbol_prices['Close']) - min(symbol_prices['Close']))
-
-    # trend deviation area
-    period = 7
-    trend_deviation = trends.trend_deviation(symbol_prices['Close'][:], period=period)
-    trend_deviation_norm = calculator.normalize(trend_deviation, low=0.0, high=normalization_factor)
-    price_offset_low = [price - vol for price, vol in zip(symbol_prices['Close'][period:], trend_deviation_norm)]
-    price_offset_high = [price + vol for price, vol in zip(symbol_prices['Close'][period:], trend_deviation_norm)]
-    ui.create_fill_area(candleFigure, dates[period:], price_offset_low, price_offset_high, "Trend Deviation", color="rgba(0,50,50,0.3)")
-
-    # trade volume area
-    volume_norm = calculator.normalize(symbol_prices['Volume'], low=0.0, high=normalization_factor)
-    log_volume_offset_low = [price - vol for price, vol in
-                             zip(symbol_prices['Close'], volume_norm)]
-    log_volume_offset_high = [price + vol for price, vol in
-                              zip(symbol_prices['Close'], volume_norm)]
-    ui.create_fill_area(candleFigure, dates, log_volume_offset_low, log_volume_offset_high, "Volume Bands", color="rgba(100,20,0,0.3)")
-
-    sma_dates, deltas_sma = trends.sma(dates, symbol_prices['Close'], period)
-    ui.create_line(candleFigure, sma_dates, deltas_sma, f"Close price {period} day SMA")
 
     # plot all
     candleFigure.update_layout(title="Symbol Ticker",
                                xaxis_title='Date',
                                yaxis_title="Price per Share",
                                template='plotly_dark')
-    selected_points = plotly_events(candleFigure, click_event=True, hover_event=False, select_event=False, key='3')
 
-    if st.checkbox('Tests'):
-        # testFigure = make_subplots(rows=1, cols=1)
-        # ui.create_line(testFigure, dates, symbol_prices['Volume'], "Volume")
-        # ui.add_mouse_indicator(testFigure, selected_points, min=np.min(symbol_prices['Volume']),
-        #                        max=np.max(symbol_prices['Volume']))
-        # testFigure.update_layout(title="Volume")
-        # test_id = st.plotly_chart(testFigure, use_container_width=True, key='test')
+    # use this to add markers on other graphs for click points on this graph
+    selected_points = []
+    # selected_points = plotly_events(candleFigure, click_event=True, hover_event=False, select_event=False, key='3', override_height=800)
+
+    st.plotly_chart(candleFigure, use_container_width=True)
+
+    if st.checkbox('Trends'):
+        period = st.slider(label='period', min_value=7, max_value=140, value=14, step=7)
+
+        indicatorFigure = make_subplots(rows=1, cols=1)
+        ui.create_line(indicatorFigure, dates, symbol_prices['Close'], "Close price", color="red")
+
+        # # indicators
+        # indicator_datasets = indicators.create_indicators(dates, symbol_prices)
+        # ui.create_indicators(fig1, indicator_datasets)
+        # trend lines
+        # sigma_multiplier = st.slider(label='min trend deviation', min_value=1.0, max_value=10.0, value=3.0, step=0.1)
+        # sigma_dates, sigma_values = trends.trend_line(symbol_prices_backtest.index, np.array(symbol_prices_backtest['Close']), min_trend_size=21, sigma_multiplier=sigma_multiplier)
+        # ui.create_markers(candleFigure, sigma_dates, sigma_values, f"{sigma_multiplier} Deviations from regression line", 'Deviations')
+
+        # vwap
+        vwap = trends.vwap(symbol_prices_backtest)
+        ui.create_line(candleFigure, backtest_dates, vwap, "Volume Weighted Average Price (VWAP)", "VWAP")
+
+        # bollinger bands
+        bollinger_dates, bollinger_low, bollinger_high = trends.bollinger_bands(backtest_dates, symbol_prices_backtest)
+        ui.create_fill_area(candleFigure, bollinger_dates, bollinger_low, bollinger_high, "Bollinger Bands")
+
+        # trend
+        trend = trends.linear_regression_trend(symbol_prices['Close'][:], period=period)
+        ui.create_line(indicatorFigure, dates[period:], trend, f"Trend: {period} day", color='blue')
+
+        # trend deviation area
+        price_deviation_from_trend = [price - trend_val for price, trend_val in
+                                      zip(symbol_prices['Close'][period:], trend)]
+        deviation_price_offset = [price + dev for price, dev in
+                                  zip(symbol_prices['Close'][period:], price_deviation_from_trend)]
+        ui.create_fill_area(indicatorFigure, dates[period:], trend, deviation_price_offset,
+                            f"Trend Deviation: {period} day trend", color="rgba(100,0,100,0.3)")
+
+        # sma line
+        deltas_sma = trends.sma(symbol_prices['Close'], period)
+        ui.create_line(indicatorFigure, dates[period:], deltas_sma, f"SMA {period} day", color='green')
+
+        # bids
+        init_val = 1000.0
+        buy_and_hold_val = bidder.buy_and_hold(init_val, symbol_prices['Close'])
+        st.write(f"BUY AND HOLD: {buy_and_hold_val}")
+
+        bid_dates, bid_vals = trends.bids(dates, symbol_prices, period)
+        strat_val = bidder.buy_rule(init_val, bid_dates, bid_vals, dates, symbol_prices['Close'])
+
+        ui.create_markers(indicatorFigure, bid_dates, bid_vals, "Bids", "Price")
+        st.write(f"STRAT: {strat_val}")
+
+        indicatorFigure.update_layout(title="Indicators",
+                                   xaxis_title='Date',
+                                   yaxis_title="Price",
+                                   template='plotly_dark')
+        st.plotly_chart(indicatorFigure, use_container_width=True)
+
+        # doxy
+        st.markdown(
+            f"Trend deviation: indicates the extent by which the price is deviating from its current trajectory. "
+            f"The trajectory is the linear regression of the price over {period} days.")
+        st.markdown('''<ol><li>mark all intercepts between the SMA and the price.</li>
+                    <li>calculate the cum-sum of trend deviations. the cum-sum is reset when an intercept occurs.</li>
+                    <li>when there is an intercept, and the cum-sum above a threshold, then a price reversal is imminent.</li></ol>''', unsafe_allow_html=True)
+
+        st.markdown('''<ol><li>if the trend intersects SMA, then reset.</li>
+                        <li>when the trend intersects the price, place a bid. if the previous intercept is lower, then buy.</li></ol>''')
+        # volumeFig = make_subplots(rows=1, cols=1)
+        # ui.create_line(volumeFig, dates, symbol_prices['Volume'], "Volume")
+        # st.plotly_chart(volumeFig, use_container_width=True)
+        # st.markdown(
+        #     "Trade Volume: a measure of investor belief. "
+        #     "A high volume means that investors have a strong belief that the price will change. "
+        #     "The volume does not indicate whether the price will go up or down.")
+
 
+    if st.checkbox('Tests'):
         deltas = [cur - symbol_prices['Close'][max(0, idx - 1)] for idx, cur in enumerate(symbol_prices['Close'])]
         testFigure2 = make_subplots(rows=1, cols=1)
         ui.create_line(testFigure2, dates, deltas, "Deltas")

trends.py

@@ -117,12 +117,11 @@ def vwap(symbol_price_data):
     return vwap_values
 
 
-def sma(dates, data_list, n_days=20):
-    res_dates, res_values = [], []
-    res_dates = dates[n_days-1:]
-    for index in range(len(data_list) - n_days + 1):
-        res_values.append(np.mean(data_list[index:index + n_days]))
-    return res_dates, res_values
+def sma(prices, period=20):
+    values = []
+    for index in range(len(prices) - period):
+        values.append(np.mean(prices[index:index + period]))
+    return values
 
 
 def bollinger_bands(dates, symbol_price_data, n_days_deviation=20, deviation_multiplier=2):
@@ -144,26 +143,42 @@ def support_lines():
     a = 1
 
 
-# the volatility is the average deviation from the current trend.
-# this is measured by doing linear regression over each period, then calculating the deviation of values from that line.
-def trend_deviation(prices, period=32):
+# a linear regression over a period
+def linear_regression_trend(prices, period=32):
     np_dates = np.array([ts.timestamp() for ts in prices.index])
     np_values = np.array(prices)
 
     value_count = len(prices)
-
-    avg_deviations = []
+    trend = []
     for index in range(value_count-period):
         np_dates_subset = np_dates[index:index+period+1]
         np_values_subset = np_values[index:index+period+1]
-        m, c = calculator.linear_regression_line(np_dates_subset, np_values_subset)
-        predicted_values = m * np_dates_subset + c
+        low, high = calculator.linear_regression(np_dates_subset, np_values_subset)
+        trend.append(high)
+
+    return trend
+
+
+def bids(dates, symbol_prices, period=14):
+    close_values = symbol_prices['Close']
+    trend = linear_regression_trend(close_values, period=period)
+    sma_values = sma(close_values, period=period)
+
+    # get intercepts
+    dates_subset = dates[period:]
+    close_subset = close_values[period:]
+
+    sma_intercept_indices = calculator.intercepts(sma_values, close_subset)
+    trend_intercept_indices = calculator.intercepts(trend, close_subset)
+
+    sma_intercept_dates = [dates_subset[index] for index in sma_intercept_indices]
+    bids = []
+    for index in sma_intercept_indices:
+        is_up = sma_values[index] > close_subset[index]
+        if is_up:
+            bids.append(10)
+        else:
+            bids.append(-10)
 
-        deviation_sum = 0
-        for value, predicted_value in zip(np_values_subset, predicted_values):
-            deviation = abs(value - predicted_value)
-            deviation_sum += deviation
-        avg_deviation = deviation_sum/period
-        avg_deviations.append(avg_deviation)
+    return sma_intercept_dates, bids
 
-    return avg_deviations

ui.py

@@ -81,8 +81,8 @@ def create_markers(fig, dates, dataset, title, y_label, marker_symbol=3, marker_
     fig.add_trace(line)
 
 
-def create_line(fig, dates, dataset, title="title", y_label="values", marker_symbol=4, marker_size=15):
-    line = go.Scatter(name=title, x=dates, y=dataset, marker_line_color="yellow")
+def create_line(fig, dates, dataset, title="title", y_label="values", marker_symbol=4, marker_size=15, color='rgba(0,100,80,0.2)'):
+    line = go.Scatter(name=title, x=dates, y=dataset, marker_line_color="yellow", fillcolor=color)
     fig.add_trace(line)