Strategy Development

Helps design, implement, and formalize trading strategies.

Real Code Reference

• tradinglearn/strategies/macd_strategy.py — Example: MACDStrategy with generate_signals(data) -> DataFrame
• tradinglearn/pytdx2/backtest.py — BaseStrategy with buy_condition() / sell_condition() / update_position()
• tradinglearn/pytdx2/evftrade/BaseStrategy.py — Abstract BaseStrategy with buy_condition(price, time) / sell_condition(price, time)
• tradinglearn/pytdx2/macd_strategy.py — MACDStrategy(BaseStrategy) implementation
• tradinglearn/pytdx2/large_trade_model.py — LargeTradeStrategy with risk controls

Process

1. Requirements — timeframe, instruments, risk tolerance, return targets
2. Signal logic — entry conditions, exit conditions, filters
3. Position sizing — fixed fraction, Kelly criterion, volatility-based
4. Risk management — stop-loss, take-profit, trailing stops
5. Implementation — code as a class with generate_signals(data) -> DataFrame
6. Validation — walk-forward analysis, out-of-sample testing, overfitting checks

Strategy Template (follow tradinlearn convention)

class MyStrategy:
    def __init__(self, param1=default, param2=default):
        self.param1 = param1
        self.param2 = param2

    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
        """Return DataFrame with 'position' column: 1=long, 0=flat, -1=short."""
        signals = pd.DataFrame(index=data.index)
        signals['position'] = 0
        # ... signal logic using data['close'], data['volume'], etc.
        return signals

Key Principles

• Never use future data in signal calculation
• Test on out-of-sample periods separate from optimization
• Account for transaction costs and slippage in backtest
• Keep strategies explainable — complexity should be justified