Analyzing Confidence Thresholds and Decision Bands for Stock Trading

Описание: Confidence thresholds and decision bands help traders act only when the predicted “edge” is large enough to survive transaction costs, slippage, and randomness in daily returns. They create an explicit no-trade zone that prevents overtrading when signals are too small to be profitable after costs. The workflow starts by downloading historical prices (MSFT), computing daily returns and next-day forward returns, and then estimating a rolling expected return using a bootstrap confidence interval over a lookback window. Trading signals are generated only when the entire confidence interval clears a positive or negative minimum edge, which is a very strict rule and often results in few trades because markets are usually close to efficient. To get more tradeable “price levels,” the approach then trains quantile Gradient Boosting models to predict return quantiles (q10/q50/q90) and converts them into next-day price levels (P10/P50/P90), forming a predictive band that represents uncertainty. A cost-aware trading plan is derived by turning those quantiles into concrete entry/exit prices and requiring the implied move to exceed a round-trip hurdle (costs plus a buffer). The strategy is evaluated by backtesting net returns with turnover-based costs and summarizing annualized return, volatility, and Sharpe versus buy-and-hold. The key insight is that tightening thresholds reduces trades but can improve net quality, while loosening thresholds increases trade frequency but risks letting costs erase the edge. To pick thresholds systematically, the notebook sweeps entry/exit hurdles and visualizes performance with a Sharpe heatmap and a Sharpe-vs-trades scatter plot to reveal the tradeoff between frequency, exposure, and profitability. Overall, the analysis turns vague trading intuition into distribution-aware, cost-aware, testable rules that can be tuned and compared objectively.