: Strategy-level safeguards that pause trading when certain thresholds are breached (daily loss >5%, maximum drawdown >15%, consecutive losses >5).
This is the most frequent mistake. Look-ahead occurs when your model is given information that would not have been available at the time of the trade. For example, using today's closing price to predict today's signal. The fix is simple: when engineering features, always use .shift(1) to align future target labels with past data points. Algorithmic Trading A-Z with Python- Machine Le...
# Example: 14-day RSI def compute_rsi(data, window=14): delta = data['Close'].diff() gain = (delta.where(delta > 0, 0)).rolling(window).mean() loss = (-delta.where(delta < 0, 0)).rolling(window).mean() rs = gain / loss return 100 - (100 / (1 + rs)) : Strategy-level safeguards that pause trading when certain
: Master coding with NumPy , Pandas , and Matplotlib for high-speed financial data analysis and visualization. For example, using today's closing price to predict
: Risking a predetermined, small percentage (e.g., 1%) of total capital on any single trade.
Backtesting is the process of simulating how a strategy would have performed on historical data. It's the single most important step before risking real capital.