Analytics of Trading and Investment Strategies

Strategies

Trend

  1. Golden Cross 50 200
  2. WMA 20 50 Crossover
  3. WMA 20 50 200 Stack
  4. Faber Timing Model
  5. WMA 13 21 34 Stack
  6. WMA 7 20 50 Stack
  7. WMA 20 50 Proximity Crossover
  8. MACD Signal Crossover
  9. WMA 50 Price Cross
  10. Price WMA 20 Crossover
  11. RSI WMA Crossover
  12. WMA 20 50 ATR Trailing Stop
  13. WMA 20 50 Short Crossover

Breakout

  1. Donchian Breakout
  2. Turtle Breakout 55 20
  3. Keltner Channel Breakout
  4. Turtle Breakout
  5. Bollinger Squeeze Breakout

Momentum

  1. Time Series Momentum
  2. Dual Momentum 90 252
  3. Skip Month Momentum
  4. Time Series Momentum 252
  5. High Watermark Momentum
  6. Momentum WMA Crossover

Mean reversion

  1. Double 7
  2. Connors RSI 2
  3. RSI Oversold Overbought
  4. Bollinger Band To Band
  5. Bollinger Mean Reversion
  6. Support Resistance Bounce

Score By Category, Timeframe, Symbol And Strategy

Best Strategy Per Category

Best Strategy Per Symbol And Timeframe

How Scoring Works

Every strategy is run on every symbol × timeframe cell, on both real and resampled price history. Each cell gets five 0–100 sub-scores, blended into one composite score by the weights below. A strategy's total score (shown next to its name) is the average of its composite score across all cells, then averaged again across the real and resampled datasets.

Beats Hold
2026-07-23T00:16:46.921075 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
Compares final net equity to what simply holding the asset would have returned over the same window. 50 is break-even; every doubling versus buy-and-hold adds 25 points.
Risk Adjusted
2026-07-23T00:16:46.933603 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
A linear read on the rolling annualized Sharpe ratio at the last trade — 0 Sharpe scores 5, roughly 1.7 Sharpe maxes out the scale.
Profitability
2026-07-23T00:16:46.945969 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
How many times the starting cash multiplied, on a log scale — each doubling of capital is worth 20 points.
Win Rate
2026-07-23T00:16:46.956684 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
The share of trades that closed profitable, rescaled so a 20% win rate scores 0 and a 60% win rate maxes out the scale.
Fee Efficiency
2026-07-23T00:16:46.969302 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
The share of the fee-free (gross) result that survives after fees — 60% survival scores 0, keeping the full gross result scores 100.
Composite
2026-07-23T00:16:46.985493 image/svg+xml Matplotlib v3.11.1, https://matplotlib.org/
The five sub-scores blended by weight into one 0–100 number per symbol × timeframe cell — risk-adjusted return and beating a hold carry the most weight.