32 KiB
32 KiB
TradingAgents: Medium-Term Enhancements (1-5 Days)
Strategic Features for Competitive Advantage
🎯 MEDIUM-TERM ENHANCEMENTS
1. Real-Time Alert System
Value: Proactive trading opportunities Effort: 2-3 days Impact: Game-changer for active traders
Use Cases:
- Price alerts (when NVDA hits $900)
- Signal alerts (when TradingAgents recommends BUY)
- Risk alerts (portfolio drops 5%)
- News alerts (breaking news about held positions)
Implementation:
# tradingagents/alerts/alert_system.py
from enum import Enum
from typing import Callable, List, Dict
from dataclasses import dataclass
from datetime import datetime
import smtplib
from twilio.rest import Client
import asyncio
class AlertType(Enum):
PRICE = "price"
SIGNAL = "signal"
RISK = "risk"
NEWS = "news"
PORTFOLIO = "portfolio"
class AlertChannel(Enum):
EMAIL = "email"
SMS = "sms"
WEBHOOK = "webhook"
PUSH = "push"
TELEGRAM = "telegram"
@dataclass
class Alert:
"""Alert configuration."""
id: str
type: AlertType
condition: Callable
channels: List[AlertChannel]
message_template: str
enabled: bool = True
cooldown: int = 300 # seconds between alerts
class AlertManager:
"""Manage trading alerts."""
def __init__(self, config: Dict):
self.config = config
self.alerts: Dict[str, Alert] = {}
self.last_triggered: Dict[str, datetime] = {}
def add_alert(self, alert: Alert):
"""Add new alert."""
self.alerts[alert.id] = alert
async def check_alerts(self, context: Dict):
"""Check all alerts against current context."""
for alert_id, alert in self.alerts.items():
if not alert.enabled:
continue
# Check cooldown
if self._is_in_cooldown(alert_id, alert.cooldown):
continue
# Evaluate condition
try:
if alert.condition(context):
await self._trigger_alert(alert, context)
self.last_triggered[alert_id] = datetime.now()
except Exception as e:
print(f"Error checking alert {alert_id}: {e}")
def _is_in_cooldown(self, alert_id: str, cooldown: int) -> bool:
"""Check if alert is in cooldown period."""
if alert_id not in self.last_triggered:
return False
elapsed = (datetime.now() - self.last_triggered[alert_id]).total_seconds()
return elapsed < cooldown
async def _trigger_alert(self, alert: Alert, context: Dict):
"""Send alert through configured channels."""
message = alert.message_template.format(**context)
tasks = []
for channel in alert.channels:
if channel == AlertChannel.EMAIL:
tasks.append(self._send_email(message))
elif channel == AlertChannel.SMS:
tasks.append(self._send_sms(message))
elif channel == AlertChannel.WEBHOOK:
tasks.append(self._send_webhook(message, context))
elif channel == AlertChannel.TELEGRAM:
tasks.append(self._send_telegram(message))
await asyncio.gather(*tasks)
async def _send_email(self, message: str):
"""Send email alert."""
smtp_server = self.config.get("smtp_server")
from_email = self.config.get("from_email")
to_email = self.config.get("alert_email")
msg = f"Subject: TradingAgents Alert\n\n{message}"
with smtplib.SMTP(smtp_server, 587) as server:
server.starttls()
server.login(from_email, self.config.get("email_password"))
server.sendmail(from_email, to_email, msg)
async def _send_sms(self, message: str):
"""Send SMS via Twilio."""
client = Client(
self.config.get("twilio_account_sid"),
self.config.get("twilio_auth_token")
)
client.messages.create(
body=message,
from_=self.config.get("twilio_from_number"),
to=self.config.get("alert_phone_number")
)
async def _send_webhook(self, message: str, context: Dict):
"""Send webhook notification."""
import aiohttp
webhook_url = self.config.get("webhook_url")
async with aiohttp.ClientSession() as session:
await session.post(
webhook_url,
json={
"message": message,
"context": context,
"timestamp": datetime.now().isoformat()
}
)
async def _send_telegram(self, message: str):
"""Send Telegram message."""
import aiohttp
bot_token = self.config.get("telegram_bot_token")
chat_id = self.config.get("telegram_chat_id")
url = f"https://api.telegram.org/bot{bot_token}/sendMessage"
async with aiohttp.ClientSession() as session:
await session.post(
url,
json={"chat_id": chat_id, "text": message}
)
# Example usage
alert_manager = AlertManager(config)
# Price alert
price_alert = Alert(
id="nvda_price_900",
type=AlertType.PRICE,
condition=lambda ctx: ctx["price"] >= 900,
channels=[AlertChannel.EMAIL, AlertChannel.SMS],
message_template="🚨 NVDA hit ${price}! Time to consider your position."
)
# Signal alert
signal_alert = Alert(
id="buy_signal",
type=AlertType.SIGNAL,
condition=lambda ctx: ctx["signal"] == "BUY" and ctx["confidence"] > 0.8,
channels=[AlertChannel.EMAIL, AlertChannel.TELEGRAM],
message_template="💰 Strong BUY signal for {ticker}: {confidence:.0%} confidence"
)
# Risk alert
risk_alert = Alert(
id="portfolio_drawdown",
type=AlertType.RISK,
condition=lambda ctx: ctx["drawdown"] > 0.05,
channels=[AlertChannel.EMAIL, AlertChannel.SMS, AlertChannel.WEBHOOK],
message_template="⚠️ Portfolio drawdown: {drawdown:.1%}. Review your positions!"
)
alert_manager.add_alert(price_alert)
alert_manager.add_alert(signal_alert)
alert_manager.add_alert(risk_alert)
# Check alerts continuously
async def monitor_loop():
while True:
context = {
"price": get_current_price("NVDA"),
"signal": get_latest_signal("NVDA"),
"confidence": get_signal_confidence("NVDA"),
"drawdown": get_portfolio_drawdown(),
"ticker": "NVDA"
}
await alert_manager.check_alerts(context)
await asyncio.sleep(60) # Check every minute
Web UI Integration:
# Add to web_app.py
@cl.on_message
async def main(message: cl.Message):
# ...
elif command == "alert":
await setup_alert(parts)
async def setup_alert(parts):
"""Setup a new alert."""
if len(parts) < 4:
await cl.Message(
content="Usage: `alert TICKER CONDITION VALUE`\n\n"
"Examples:\n"
"- `alert NVDA price 900` - Alert when NVDA hits $900\n"
"- `alert AAPL buy 0.8` - Alert on BUY signal with 80%+ confidence\n"
"- `alert portfolio drop 5` - Alert on 5% drawdown"
).send()
return
ticker = parts[1].upper()
condition_type = parts[2].lower()
threshold = float(parts[3])
# Create alert
alert = create_alert(ticker, condition_type, threshold)
alert_manager.add_alert(alert)
await cl.Message(
content=f"✅ Alert created!\n\n"
f"**Ticker:** {ticker}\n"
f"**Condition:** {condition_type} {threshold}\n"
f"**Channels:** Email, Telegram\n\n"
f"You'll be notified when this condition is met."
).send()
2. Interactive Broker Integration
Value: Access to professional trading Effort: 3-4 days Impact: Opens door to serious traders
Implementation:
# tradingagents/brokers/ib_broker.py
from ib_insync import IB, Stock, MarketOrder, LimitOrder
from decimal import Decimal
from .base import BaseBroker, BrokerOrder, BrokerPosition, BrokerAccount
class InteractiveBrokersBroker(BaseBroker):
"""Interactive Brokers integration."""
def __init__(
self,
host: str = "127.0.0.1",
port: int = 7497, # 7497 for paper, 7496 for live
client_id: int = 1,
paper_trading: bool = True
):
super().__init__(paper_trading)
self.host = host
self.port = port
self.client_id = client_id
self.ib = IB()
def connect(self) -> bool:
"""Connect to IB Gateway or TWS."""
try:
self.ib.connect(self.host, self.port, clientId=self.client_id)
self.connected = True
return True
except Exception as e:
raise ConnectionError(f"Failed to connect to IB: {e}")
def disconnect(self) -> None:
"""Disconnect from IB."""
self.ib.disconnect()
self.connected = False
def get_account(self) -> BrokerAccount:
"""Get account information."""
account_values = self.ib.accountValues()
cash = Decimal(0)
equity = Decimal(0)
buying_power = Decimal(0)
for value in account_values:
if value.tag == "CashBalance":
cash = Decimal(value.value)
elif value.tag == "NetLiquidation":
equity = Decimal(value.value)
elif value.tag == "BuyingPower":
buying_power = Decimal(value.value)
return BrokerAccount(
account_number=self.ib.wrapper.accounts[0],
cash=cash,
buying_power=buying_power,
portfolio_value=equity,
equity=equity,
last_equity=equity,
multiplier=Decimal("1"),
)
def get_positions(self) -> List[BrokerPosition]:
"""Get all positions."""
positions = []
for position in self.ib.positions():
current_price = self._get_last_price(position.contract)
positions.append(BrokerPosition(
symbol=position.contract.symbol,
quantity=Decimal(str(position.position)),
avg_entry_price=Decimal(str(position.avgCost)),
current_price=current_price,
market_value=current_price * Decimal(str(position.position)),
unrealized_pnl=Decimal(str(position.unrealizedPNL)),
unrealized_pnl_percent=(
Decimal(str(position.unrealizedPNL)) /
Decimal(str(position.avgCost * position.position))
),
cost_basis=Decimal(str(position.avgCost * position.position)),
))
return positions
def submit_order(self, order: BrokerOrder) -> BrokerOrder:
"""Submit order to IB."""
contract = Stock(order.symbol, "SMART", "USD")
if order.order_type == OrderType.MARKET:
ib_order = MarketOrder(
"BUY" if order.side == OrderSide.BUY else "SELL",
float(order.quantity)
)
elif order.order_type == OrderType.LIMIT:
ib_order = LimitOrder(
"BUY" if order.side == OrderSide.BUY else "SELL",
float(order.quantity),
float(order.limit_price)
)
trade = self.ib.placeOrder(contract, ib_order)
order.order_id = str(trade.order.orderId)
order.status = self._convert_ib_status(trade.orderStatus.status)
return order
def _get_last_price(self, contract) -> Decimal:
"""Get last traded price."""
ticker = self.ib.reqTickers(contract)[0]
return Decimal(str(ticker.last))
# Example usage
ib_broker = InteractiveBrokersBroker(
host="127.0.0.1",
port=7497, # Paper trading
paper_trading=True
)
ib_broker.connect()
account = ib_broker.get_account()
print(f"IB Account: ${account.equity:,.2f}")
Configuration:
# .env.example
# Interactive Brokers
IB_HOST=127.0.0.1
IB_PORT=7497 # 7497 for paper, 7496 for live
IB_CLIENT_ID=1
3. Advanced Charting System
Value: Professional-grade visualization Effort: 3-4 days Impact: Traders love charts
Implementation using Plotly:
# tradingagents/visualization/charts.py
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
class TradingCharts:
"""Advanced charting for TradingAgents."""
@staticmethod
def create_candlestick_with_signals(
df: pd.DataFrame,
signals: pd.DataFrame,
indicators: Dict = None
):
"""Create interactive candlestick chart with trading signals."""
fig = make_subplots(
rows=3, cols=1,
shared_xaxes=True,
vertical_spacing=0.03,
row_heights=[0.6, 0.2, 0.2],
subplot_titles=('Price & Signals', 'Volume', 'Indicators')
)
# Candlestick
fig.add_trace(
go.Candlestick(
x=df.index,
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price'
),
row=1, col=1
)
# Buy signals
buy_signals = signals[signals['action'] == 'buy']
fig.add_trace(
go.Scatter(
x=buy_signals.index,
y=buy_signals['price'],
mode='markers',
marker=dict(
symbol='triangle-up',
size=15,
color='green'
),
name='Buy Signal'
),
row=1, col=1
)
# Sell signals
sell_signals = signals[signals['action'] == 'sell']
fig.add_trace(
go.Scatter(
x=sell_signals.index,
y=sell_signals['price'],
mode='markers',
marker=dict(
symbol='triangle-down',
size=15,
color='red'
),
name='Sell Signal'
),
row=1, col=1
)
# Moving averages
if indicators and 'sma_20' in indicators:
fig.add_trace(
go.Scatter(
x=df.index,
y=indicators['sma_20'],
name='SMA 20',
line=dict(color='orange', width=1)
),
row=1, col=1
)
if indicators and 'sma_50' in indicators:
fig.add_trace(
go.Scatter(
x=df.index,
y=indicators['sma_50'],
name='SMA 50',
line=dict(color='blue', width=1)
),
row=1, col=1
)
# Volume
colors = ['red' if close < open else 'green'
for close, open in zip(df['close'], df['open'])]
fig.add_trace(
go.Bar(
x=df.index,
y=df['volume'],
marker_color=colors,
name='Volume',
showlegend=False
),
row=2, col=1
)
# RSI
if indicators and 'rsi' in indicators:
fig.add_trace(
go.Scatter(
x=df.index,
y=indicators['rsi'],
name='RSI',
line=dict(color='purple')
),
row=3, col=1
)
# RSI zones
fig.add_hline(y=70, line_dash="dash", line_color="red", row=3, col=1)
fig.add_hline(y=30, line_dash="dash", line_color="green", row=3, col=1)
# Layout
fig.update_layout(
title='TradingAgents Analysis',
xaxis_rangeslider_visible=False,
height=800,
hovermode='x unified',
template='plotly_dark'
)
fig.update_xaxes(title_text="Date", row=3, col=1)
fig.update_yaxes(title_text="Price", row=1, col=1)
fig.update_yaxes(title_text="Volume", row=2, col=1)
fig.update_yaxes(title_text="RSI", row=3, col=1)
return fig
@staticmethod
def create_portfolio_dashboard(portfolio_data: Dict):
"""Create comprehensive portfolio dashboard."""
fig = make_subplots(
rows=2, cols=2,
subplot_titles=(
'Portfolio Value Over Time',
'Position Allocation',
'P&L by Position',
'Win Rate Analysis'
),
specs=[
[{"type": "scatter"}, {"type": "pie"}],
[{"type": "bar"}, {"type": "bar"}]
]
)
# Portfolio equity curve
fig.add_trace(
go.Scatter(
x=portfolio_data['dates'],
y=portfolio_data['equity'],
mode='lines',
name='Portfolio Value',
fill='tozeroy'
),
row=1, col=1
)
# Position allocation pie
fig.add_trace(
go.Pie(
labels=portfolio_data['positions']['symbols'],
values=portfolio_data['positions']['values'],
name='Allocation'
),
row=1, col=2
)
# P&L by position
colors = ['green' if pnl > 0 else 'red'
for pnl in portfolio_data['positions']['pnl']]
fig.add_trace(
go.Bar(
x=portfolio_data['positions']['symbols'],
y=portfolio_data['positions']['pnl'],
marker_color=colors,
name='P&L'
),
row=2, col=1
)
# Win rate
fig.add_trace(
go.Bar(
x=['Wins', 'Losses'],
y=[
portfolio_data['wins'],
portfolio_data['losses']
],
marker_color=['green', 'red'],
name='Trades'
),
row=2, col=2
)
fig.update_layout(
height=800,
showlegend=True,
template='plotly_dark'
)
return fig
# Integration with web UI
@cl.on_message
async def main(message: cl.Message):
# ...
elif command == "chart":
await show_chart(parts)
async def show_chart(parts):
"""Show interactive chart."""
if len(parts) < 2:
await cl.Message(content="Usage: `chart TICKER`").send()
return
ticker = parts[1].upper()
# Fetch data
df = get_stock_data(ticker, days=90)
signals = get_trading_signals(ticker)
indicators = calculate_indicators(df)
# Create chart
fig = TradingCharts.create_candlestick_with_signals(
df, signals, indicators
)
# Send to user
await cl.Message(
content=f"📊 Chart for {ticker}",
elements=[cl.Plotly(name="chart", figure=fig)]
).send()
4. Strategy Backtesting UI
Value: Visual strategy optimization Effort: 2-3 days Impact: Makes backtesting accessible
Implementation:
# Add to web_app.py
@cl.on_message
async def main(message: cl.Message):
# ...
elif command == "backtest":
await run_backtest_ui(parts)
async def run_backtest_ui(parts):
"""Interactive backtesting interface."""
if len(parts) < 4:
await cl.Message(
content="""# 📊 Backtest Your Strategy
Usage: `backtest TICKER START_DATE END_DATE`
Example:
`backtest NVDA 2023-01-01 2024-01-01`
This will:
1. Run TradingAgents on historical data
2. Show performance metrics
3. Generate interactive charts
4. Compare to buy-and-hold
"""
).send()
return
ticker = parts[1].upper()
start_date = parts[2]
end_date = parts[3]
# Show progress
progress_msg = await cl.Message(
content=f"🔄 Running backtest for {ticker}...\n\n"
"This may take a few minutes."
).send()
try:
# Run backtest
from tradingagents.backtest import backtest_trading_agents
results = await asyncio.to_thread(
backtest_trading_agents,
trading_graph=ta_graph,
tickers=[ticker],
start_date=start_date,
end_date=end_date,
initial_capital=100000.0
)
# Create visualizations
equity_fig = create_equity_curve(results)
metrics_fig = create_metrics_dashboard(results)
# Send results
await cl.Message(
content=f"""# 📊 Backtest Results: {ticker}
## Performance Summary
**Period:** {start_date} to {end_date}
### Returns
- **Total Return:** {results.total_return:.2%}
- **Annualized:** {results.annualized_return:.2%}
- **Benchmark (Buy & Hold):** {results.benchmark_return:.2%}
- **Alpha:** {results.alpha:.2%}
### Risk Metrics
- **Sharpe Ratio:** {results.sharpe_ratio:.2f}
- **Max Drawdown:** {results.max_drawdown:.2%}
- **Volatility:** {results.volatility:.2%}
### Trading Stats
- **Total Trades:** {results.total_trades}
- **Win Rate:** {results.win_rate:.1%}
- **Profit Factor:** {results.profit_factor:.2f}
- **Average Win:** ${results.avg_win:,.2f}
- **Average Loss:** ${results.avg_loss:,.2f}
## Charts
""",
elements=[
cl.Plotly(name="equity", figure=equity_fig),
cl.Plotly(name="metrics", figure=metrics_fig)
]
).send()
# Offer to save
await cl.Message(
content="💾 Save this report? Type `save report {ticker}_backtest`"
).send()
except Exception as e:
await cl.Message(
content=f"❌ Backtest failed: {str(e)}\n\n"
"Check your date range and ticker symbol."
).send()
5. Multi-Ticker Portfolio Mode
Value: Diversification support Effort: 2-3 days Impact: Professional portfolio management
Implementation:
# tradingagents/portfolio/multi_ticker.py
from typing import List, Dict
import asyncio
from decimal import Decimal
class MultiTickerPortfolio:
"""Manage multiple tickers simultaneously."""
def __init__(
self,
tickers: List[str],
allocation: Dict[str, float] = None,
rebalance_frequency: str = "monthly"
):
self.tickers = tickers
self.allocation = allocation or self._equal_weight()
self.rebalance_frequency = rebalance_frequency
def _equal_weight(self) -> Dict[str, float]:
"""Equal weight allocation."""
weight = 1.0 / len(self.tickers)
return {ticker: weight for ticker in self.tickers}
async def analyze_all(self, date: str) -> Dict[str, any]:
"""Analyze all tickers in parallel."""
tasks = [
self._analyze_ticker(ticker, date)
for ticker in self.tickers
]
results = await asyncio.gather(*tasks)
return {
ticker: result
for ticker, result in zip(self.tickers, results)
}
async def _analyze_ticker(self, ticker: str, date: str):
"""Analyze single ticker."""
_, signal = await ta_graph.propagate_async(ticker, date)
return signal
def calculate_portfolio_signals(
self,
signals: Dict[str, str]
) -> Dict[str, Decimal]:
"""
Calculate position sizes based on signals and allocation.
Returns:
Dict mapping ticker to target quantity
"""
positions = {}
for ticker, signal in signals.items():
target_allocation = self.allocation[ticker]
if signal == "BUY":
# Increase position to target allocation
positions[ticker] = self._calculate_target_shares(
ticker, target_allocation
)
elif signal == "SELL":
# Reduce position
positions[ticker] = Decimal(0)
elif signal == "HOLD":
# Maintain current position
positions[ticker] = self._get_current_position(ticker)
return positions
def rebalance(self, portfolio_value: Decimal):
"""Rebalance portfolio to target allocations."""
for ticker, target_pct in self.allocation.items():
target_value = portfolio_value * Decimal(str(target_pct))
current_value = self._get_position_value(ticker)
difference = target_value - current_value
if abs(difference) > portfolio_value * Decimal("0.05"): # 5% threshold
# Need to rebalance
shares_to_trade = difference / self._get_current_price(ticker)
yield ticker, shares_to_trade
# Usage in web UI
@cl.on_message
async def main(message: cl.Message):
# ...
elif command == "portfolio-analyze":
await analyze_portfolio(parts)
async def analyze_portfolio(parts):
"""Analyze entire portfolio."""
# Get user's portfolio
tickers = ["NVDA", "AAPL", "MSFT", "GOOGL", "TSLA"] # From config
await cl.Message(
content=f"🔍 Analyzing portfolio: {', '.join(tickers)}\n\n"
"This will take 2-3 minutes..."
).send()
# Analyze all in parallel
portfolio = MultiTickerPortfolio(tickers)
signals = await portfolio.analyze_all(date="2024-05-10")
# Format results
result = "# 📊 Portfolio Analysis Results\n\n"
for ticker, signal in signals.items():
emoji = "🟢" if signal == "BUY" else "🔴" if signal == "SELL" else "🟡"
result += f"{emoji} **{ticker}**: {signal}\n"
result += "\n## Recommendations\n\n"
# Calculate suggested trades
positions = portfolio.calculate_portfolio_signals(signals)
for ticker, target_qty in positions.items():
current_qty = get_current_position(ticker)
difference = target_qty - current_qty
if difference > 0:
result += f"- Buy {difference} shares of {ticker}\n"
elif difference < 0:
result += f"- Sell {abs(difference)} shares of {ticker}\n"
await cl.Message(content=result).send()
6. Decision History Database
Value: Learn from past decisions Effort: 2-3 days Impact: Enables analysis and improvement
Implementation:
# tradingagents/history/decision_db.py
import sqlite3
from datetime import datetime
from typing import Dict, List
import json
class DecisionDatabase:
"""Store and analyze trading decisions."""
def __init__(self, db_path: str = "tradingagents_decisions.db"):
self.db_path = db_path
self._init_db()
def _init_db(self):
"""Initialize database schema."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS decisions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT NOT NULL,
ticker TEXT NOT NULL,
date TEXT NOT NULL,
signal TEXT NOT NULL,
confidence REAL,
market_price REAL,
decision_data TEXT,
analyst_reports TEXT,
execution_price REAL,
execution_time TEXT,
outcome TEXT,
pnl REAL,
created_at TEXT DEFAULT CURRENT_TIMESTAMP
)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_ticker_date
ON decisions(ticker, date)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_signal
ON decisions(signal)
""")
conn.commit()
conn.close()
def record_decision(
self,
ticker: str,
date: str,
signal: str,
confidence: float,
market_price: float,
decision_data: Dict,
analyst_reports: Dict
) -> int:
"""Record a trading decision."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
INSERT INTO decisions (
timestamp, ticker, date, signal, confidence,
market_price, decision_data, analyst_reports
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
""", (
datetime.now().isoformat(),
ticker,
date,
signal,
confidence,
market_price,
json.dumps(decision_data),
json.dumps(analyst_reports)
))
decision_id = cursor.lastrowid
conn.commit()
conn.close()
return decision_id
def update_outcome(
self,
decision_id: int,
execution_price: float,
execution_time: str,
outcome: str,
pnl: float
):
"""Update decision outcome."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
UPDATE decisions
SET execution_price = ?,
execution_time = ?,
outcome = ?,
pnl = ?
WHERE id = ?
""", (execution_price, execution_time, outcome, pnl, decision_id))
conn.commit()
conn.close()
def get_decision_history(
self,
ticker: str = None,
signal: str = None,
limit: int = 100
) -> List[Dict]:
"""Query decision history."""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
query = "SELECT * FROM decisions WHERE 1=1"
params = []
if ticker:
query += " AND ticker = ?"
params.append(ticker)
if signal:
query += " AND signal = ?"
params.append(signal)
query += " ORDER BY timestamp DESC LIMIT ?"
params.append(limit)
cursor.execute(query, params)
rows = cursor.fetchall()
conn.close()
return [dict(row) for row in rows]
def analyze_performance(self, ticker: str = None) -> Dict:
"""Analyze decision performance."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
query = """
SELECT
signal,
COUNT(*) as total,
AVG(confidence) as avg_confidence,
SUM(CASE WHEN outcome = 'win' THEN 1 ELSE 0 END) as wins,
SUM(CASE WHEN outcome = 'loss' THEN 1 ELSE 0 END) as losses,
AVG(pnl) as avg_pnl,
SUM(pnl) as total_pnl
FROM decisions
WHERE outcome IS NOT NULL
"""
if ticker:
query += " AND ticker = ?"
cursor.execute(query + " GROUP BY signal", (ticker,))
else:
cursor.execute(query + " GROUP BY signal")
results = cursor.fetchall()
conn.close()
analysis = {}
for row in results:
signal = row[0]
analysis[signal] = {
"total": row[1],
"avg_confidence": row[2],
"wins": row[3],
"losses": row[4],
"win_rate": row[3] / row[1] if row[1] > 0 else 0,
"avg_pnl": row[5],
"total_pnl": row[6]
}
return analysis
# Integration
decision_db = DecisionDatabase()
# After analysis
decision_id = decision_db.record_decision(
ticker="NVDA",
date="2024-05-10",
signal="BUY",
confidence=0.85,
market_price=880.50,
decision_data=final_state,
analyst_reports={
"fundamentals": fundamentals_report,
"news": news_report,
"technical": technical_report
}
)
# After trade execution
decision_db.update_outcome(
decision_id=decision_id,
execution_price=881.25,
execution_time="2024-05-10T10:30:00",
outcome="win", # or "loss"
pnl=245.00
)
# Analyze performance
performance = decision_db.analyze_performance(ticker="NVDA")
print(f"NVDA BUY signals win rate: {performance['BUY']['win_rate']:.1%}")
Continued in STRATEGIC_INITIATIVES.md →