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Signal Engine Database Schema
Audit-grade SQLite schema with full batch commitment ledger
"""
from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Boolean, Text, ForeignKey, Index, JSON
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship
from datetime import datetime
import hashlib
import json
Base = declarative_base()
class Symbol(Base):
__tablename__ = 'symbols'
id = Column(Integer, primary_key=True)
symbol = Column(String(50), unique=True, nullable=False, index=True)
name = Column(String(100))
min_price = Column(Float, default=0.10)
min_volume_24h = Column(Float, default=50000.0)
active = Column(Boolean, default=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
candles = relationship("Candle", back_populates="symbol_obj")
predictions = relationship("Prediction", back_populates="symbol_obj")
class Candle(Base):
__tablename__ = 'candles'
id = Column(Integer, primary_key=True)
symbol = Column(String(50), nullable=False, index=True)
timestamp = Column(DateTime, nullable=False, index=True)
open = Column(Float, nullable=False)
high = Column(Float, nullable=False)
low = Column(Float, nullable=False)
close = Column(Float, nullable=False)
volume = Column(Float, nullable=False)
quote_volume = Column(Float, nullable=False)
symbol_obj = relationship("Symbol", back_populates="candles")
__table_args__ = (
Index('idx_symbol_timestamp', 'symbol', 'timestamp'),
)
class PredictionBatch(Base):
__tablename__ = 'prediction_batches'
id = Column(Integer, primary_key=True)
batch_id = Column(String(64), unique=True, nullable=False, index=True)
target_hour = Column(DateTime, nullable=False, index=True)
committed_at = Column(DateTime, default=datetime.utcnow, nullable=False)
# Full batch commitment hash (SHA-256 of canonical JSON)
batch_hash = Column(String(64), nullable=False, index=True)
# Previous batch hash for chain verification
prev_batch_hash = Column(String(64), nullable=True, index=True)
# Model version info
model_version = Column(String(50))
ensemble_weights = Column(JSON)
# Batch metadata
num_predictions = Column(Integer, default=0)
avg_confidence = Column(Float)
# Scoring (filled later)
scored_at = Column(DateTime, nullable=True)
accuracy = Column(Float, nullable=True)
brier_score = Column(Float, nullable=True)
hypothetical_pnl_bps = Column(Float, nullable=True)
predictions = relationship("Prediction", back_populates="batch")
__table_args__ = (
Index('idx_target_hour', 'target_hour'),
Index('idx_committed_at', 'committed_at'),
)
class Prediction(Base):
__tablename__ = 'predictions'
id = Column(Integer, primary_key=True)
batch_id = Column(String(64), nullable=False, index=True)
symbol = Column(String(50), nullable=False, index=True)
# Target info
target_hour = Column(DateTime, nullable=False, index=True)
entry_price = Column(Float, nullable=False)
# Model output
direction = Column(String(10), nullable=False) # LONG, SHORT, FLAT
probability_up = Column(Float, nullable=False)
confidence = Column(Float, nullable=False)
suggested_position = Column(Float, nullable=False) # -1 to 1
# Feature hash for reproducibility
feature_hash = Column(String(64), nullable=False)
# Scoring (filled later)
exit_price = Column(Float, nullable=True)
actual_return = Column(Float, nullable=True)
correct = Column(Boolean, nullable=True)
scored_at = Column(DateTime, nullable=True)
batch = relationship("PredictionBatch", back_populates="predictions")
symbol_obj = relationship("Symbol", back_populates="predictions")
__table_args__ = (
Index('idx_symbol_target', 'symbol', 'target_hour'),
)
class LedgerEntry(Base):
__tablename__ = 'ledger'
id = Column(Integer, primary_key=True)
entry_hash = Column(String(64), unique=True, nullable=False, index=True)
prev_hash = Column(String(64), nullable=True, index=True)
entry_type = Column(String(20), nullable=False) # BATCH_COMMIT, BATCH_SCORE, KEY_ISSUE, etc.
timestamp = Column(DateTime, default=datetime.utcnow, nullable=False, index=True)
# Full payload for audit
payload = Column(Text, nullable=False)
payload_hash = Column(String(64), nullable=False)
# Signature (if applicable)
signature = Column(String(128), nullable=True)
__table_args__ = (
Index('idx_timestamp', 'timestamp'),
Index('idx_entry_type', 'entry_type'),
)
class Subscriber(Base):
__tablename__ = 'subscribers'
id = Column(Integer, primary_key=True)
email = Column(String(255), unique=True, nullable=False, index=True)
stripe_customer_id = Column(String(100), nullable=True, index=True)
# Subscription status
tier = Column(String(20), default='free') # free, pro, enterprise
active = Column(Boolean, default=True)
expires_at = Column(DateTime, nullable=True)
# API access
api_key = Column(String(64), unique=True, nullable=True, index=True)
api_key_hash = Column(String(64), nullable=True, index=True)
rate_limit_per_hour = Column(Integer, default=100)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
class ApiUsage(Base):
__tablename__ = 'api_usage'
id = Column(Integer, primary_key=True)
subscriber_id = Column(Integer, ForeignKey('subscribers.id'), nullable=False, index=True)
endpoint = Column(String(100), nullable=False)
timestamp = Column(DateTime, default=datetime.utcnow, index=True)
status_code = Column(Integer, nullable=False)
__table_args__ = (
Index('idx_subscriber_timestamp', 'subscriber_id', 'timestamp'),
)
class ModelState(Base):
__tablename__ = 'model_state'
id = Column(Integer, primary_key=True)
model_name = Column(String(50), unique=True, nullable=False, index=True)
version = Column(String(50), nullable=False)
# Serialized model (pickle bytes)
model_blob = Column(Text, nullable=False)
# Model metadata
trained_at = Column(DateTime, nullable=False)
training_samples = Column(Integer, default=0)
feature_importance = Column(JSON, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
def compute_batch_hash(predictions: list, metadata: dict) -> str:
"""Compute SHA-256 hash of canonical prediction batch"""
canonical = {
'predictions': sorted(predictions, key=lambda x: (x['symbol'], x['target_hour'])),
'metadata': metadata
}
canonical_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
return hashlib.sha256(canonical_str.encode()).hexdigest()
def compute_ledger_entry(entry_type: str, payload: dict, prev_hash: str = None) -> tuple:
"""Compute ledger entry hash and return entry data"""
canonical = {
'type': entry_type,
'timestamp': datetime.utcnow().isoformat(),
'payload': payload,
'prev_hash': prev_hash
}
canonical_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
entry_hash = hashlib.sha256(canonical_str.encode()).hexdigest()
payload_hash = hashlib.sha256(json.dumps(payload, sort_keys=True).encode()).hexdigest()
return entry_hash, payload_hash, canonical_str
def init_db(db_url: str = "sqlite:///signal_engine.db"):
"""Initialize database with all tables"""
engine = create_engine(db_url)
Base.metadata.create_all(engine)
return engine
def get_session(db_url: str = "sqlite:///signal_engine.db"):
"""Get database session"""
engine = create_engine(db_url)
SessionLocal = sessionmaker(bind=engine)
return SessionLocal()
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