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a9237fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 | import sqlalchemy as sa
from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Text, Boolean, text
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from datetime import datetime
import json
import pandas as pd
Base = declarative_base()
class StockData(Base):
__tablename__ = 'stock_data'
id = Column(Integer, primary_key=True)
symbol = Column(String(20), nullable=False)
date = Column(DateTime, nullable=False)
open_price = Column(Float)
high_price = Column(Float)
low_price = Column(Float)
close_price = Column(Float)
volume = Column(Integer)
created_at = Column(DateTime, default=datetime.now)
class TechnicalIndicators(Base):
__tablename__ = 'technical_indicators'
id = Column(Integer, primary_key=True)
symbol = Column(String(20), nullable=False)
date = Column(DateTime, nullable=False)
rsi = Column(Float)
macd = Column(Float)
macd_signal = Column(Float)
bb_upper = Column(Float)
bb_middle = Column(Float)
bb_lower = Column(Float)
ema_20 = Column(Float)
sma_20 = Column(Float)
sma_50 = Column(Float)
stoch_k = Column(Float)
williams_r = Column(Float)
atr = Column(Float)
created_at = Column(DateTime, default=datetime.now)
class TradingSignals(Base):
__tablename__ = 'trading_signals'
id = Column(Integer, primary_key=True)
symbol = Column(String(20), nullable=False)
signal_date = Column(DateTime, nullable=False)
signal = Column(String(10)) # BUY, SELL, HOLD
confidence = Column(Float)
live_price = Column(Float)
predicted_return = Column(Float)
model_version = Column(String(50))
created_at = Column(DateTime, default=datetime.now)
class ModelMetrics(Base):
__tablename__ = 'model_metrics'
id = Column(Integer, primary_key=True)
symbol = Column(String(20), nullable=False)
model_type = Column(String(50))
accuracy = Column(Float)
precision_score = Column(Float)
recall_score = Column(Float)
training_date = Column(DateTime, default=datetime.now)
feature_count = Column(Integer)
training_samples = Column(Integer)
is_active = Column(Boolean, default=True)
class DatabaseManager:
def __init__(self):
# SQLite persistent database for better data retention
self.engine = create_engine('sqlite:///stock_trading.db', echo=False)
try:
# Create all tables
Base.metadata.create_all(self.engine)
Session = sessionmaker(bind=self.engine)
self.session = Session()
# Verify tables were created
with self.engine.connect() as conn:
result = conn.execute(text("SELECT name FROM sqlite_master WHERE type='table'"))
tables = [row[0] for row in result]
print(f"Database tables available: {tables}")
except Exception as e:
print(f"Database initialization error: {str(e)}")
raise e
def save_stock_data(self, symbol, data_df):
"""Save stock OHLCV data to database"""
try:
for index, row in data_df.iterrows():
stock_record = StockData(
symbol=symbol,
date=index,
open_price=float(row['Open']),
high_price=float(row['High']),
low_price=float(row['Low']),
close_price=float(row['Close']),
volume=int(row['Volume'])
)
self.session.merge(stock_record)
self.session.commit()
return True
except Exception as e:
self.session.rollback()
print(f"Error saving stock data: {str(e)}")
return False
def save_technical_indicators(self, symbol, indicators_df):
"""Save technical indicators to database"""
try:
for index, row in indicators_df.iterrows():
indicator_record = TechnicalIndicators(
symbol=symbol,
date=index,
rsi=float(row.get('RSI', 0)) if pd.notna(row.get('RSI', 0)) else None,
macd=float(row.get('MACD', 0)) if pd.notna(row.get('MACD', 0)) else None,
macd_signal=float(row.get('MACD_signal', 0)) if pd.notna(row.get('MACD_signal', 0)) else None,
bb_upper=float(row.get('BB_upper', 0)) if pd.notna(row.get('BB_upper', 0)) else None,
bb_middle=float(row.get('BB_middle', 0)) if pd.notna(row.get('BB_middle', 0)) else None,
bb_lower=float(row.get('BB_lower', 0)) if pd.notna(row.get('BB_lower', 0)) else None,
ema_20=float(row.get('EMA_20', 0)) if pd.notna(row.get('EMA_20', 0)) else None,
sma_20=float(row.get('SMA_20', 0)) if pd.notna(row.get('SMA_20', 0)) else None,
sma_50=float(row.get('SMA_50', 0)) if pd.notna(row.get('SMA_50', 0)) else None,
stoch_k=float(row.get('Stoch_K', 0)) if pd.notna(row.get('Stoch_K', 0)) else None,
williams_r=float(row.get('Williams_R', 0)) if pd.notna(row.get('Williams_R', 0)) else None,
atr=float(row.get('ATR', 0)) if pd.notna(row.get('ATR', 0)) else None
)
self.session.merge(indicator_record)
self.session.commit()
return True
except Exception as e:
self.session.rollback()
print(f"Error saving technical indicators: {str(e)}")
return False
def save_trading_signal(self, symbol, signal, confidence, live_price, predicted_return=None, model_version="v1.0"):
"""Save trading signal to database"""
try:
signal_record = TradingSignals(
symbol=symbol,
signal_date=datetime.now(),
signal=signal,
confidence=confidence,
live_price=live_price,
predicted_return=predicted_return,
model_version=model_version
)
self.session.add(signal_record)
self.session.commit()
return True
except Exception as e:
self.session.rollback()
print(f"Error saving trading signal: {str(e)}")
return False
def save_model_metrics(self, symbol, model_type, accuracy, precision, recall, feature_count, training_samples):
"""Save model performance metrics to database"""
try:
# Deactivate old models for this symbol
old_models = self.session.query(ModelMetrics).filter_by(symbol=symbol, is_active=True).all()
for model in old_models:
model.is_active = False
# Add new model metrics
metrics_record = ModelMetrics(
symbol=symbol,
model_type=model_type,
accuracy=accuracy,
precision_score=precision,
recall_score=recall,
feature_count=feature_count,
training_samples=training_samples,
is_active=True
)
self.session.add(metrics_record)
self.session.commit()
return True
except Exception as e:
self.session.rollback()
print(f"Error saving model metrics: {str(e)}")
return False
def get_latest_stock_data(self, symbol, limit=100):
"""Get latest stock data from database"""
try:
records = self.session.query(StockData).filter_by(symbol=symbol)\
.order_by(StockData.date.desc()).limit(limit).all()
data = []
for record in records:
data.append({
'Date': record.date,
'Open': record.open_price,
'High': record.high_price,
'Low': record.low_price,
'Close': record.close_price,
'Volume': record.volume
})
return pd.DataFrame(data).set_index('Date').sort_index()
except Exception as e:
print(f"Error getting stock data: {str(e)}")
return pd.DataFrame()
def get_trading_signals_history(self, symbol, limit=50):
"""Get trading signals history for a symbol"""
try:
records = self.session.query(TradingSignals).filter_by(symbol=symbol)\
.order_by(TradingSignals.signal_date.desc()).limit(limit).all()
signals = []
for record in records:
signals.append({
'Date': record.signal_date,
'Signal': record.signal,
'Confidence': record.confidence,
'Live Price': record.live_price,
'Model Version': record.model_version
})
return pd.DataFrame(signals)
except Exception as e:
print(f"Error getting trading signals: {str(e)}")
return pd.DataFrame()
def get_model_performance(self, symbol):
"""Get current model performance metrics"""
try:
record = self.session.query(ModelMetrics).filter_by(symbol=symbol, is_active=True).first()
if record:
return {
'model_type': record.model_type,
'accuracy': record.accuracy,
'precision': record.precision_score,
'recall': record.recall_score,
'training_date': record.training_date,
'feature_count': record.feature_count,
'training_samples': record.training_samples
}
return None
except Exception as e:
print(f"Error getting model performance: {str(e)}")
return None
def get_database_stats(self):
"""Get database statistics"""
try:
stats = {
'total_stock_records': self.session.query(StockData).count(),
'total_indicators': self.session.query(TechnicalIndicators).count(),
'total_signals': self.session.query(TradingSignals).count(),
'active_models': self.session.query(ModelMetrics).filter_by(is_active=True).count(),
'symbols_tracked': len(self.session.query(StockData.symbol).distinct().all())
}
return stats
except Exception as e:
print(f"Error getting database stats: {str(e)}")
return {}
def close(self):
"""Close database connection"""
self.session.close() |