QC Deep Feature Forensics
Go beyond P&L analysis. This skill answers: "What market microstructure conditions were present when winning trades were entered vs losing trades?"
When to use
- "Why do some of my trades win and others lose?"
- "What entry conditions lead to profitable trades?"
- "Run a feature attribution on my backtest"
- "What-if analysis: would filtering out RSI > 70 trades help?"
- After running
qc-order-forensicsfor high-level diagnosis, use this for deep-dive
How it works
python3 deep_forensics.py <orders.csv>
Pipeline
- Order Reconstruction: Groups raw orders into closed trade pairs (buy group + sell group per contract)
- Batch Data Download: Fetches historical daily OHLCV from Yahoo Finance per ticker (cached to local CSV to avoid re-downloading)
- Technical Indicator Pre-computation: Calculates all 12 indicators on the full ticker history
- Feature Extraction: For each trade, extracts a 12-dimensional feature vector at the entry date
- Winner vs Loser Comparison: Statistical dual-sample comparison with diagnostic interpretation
- What-If Simulation: Tests hypothetical filters and measures their net impact on total P&L
12 Feature Factors
| Factor | Description | Format |
|---|---|---|
gap_pct |
Gap open % vs previous close | % |
volume_surge |
Volume / 10-day average volume | x |
ma5_deviation |
(Close - MA5) / MA5 | % |
ma20_deviation |
(Close - MA20) / MA20 | % |
volatility_expansion |
Intraday range / 5-day ATR | x |
intraday_return |
(Close - Open) / Open | % |
rsi_14 |
RSI(14) | 0-100 |
bb_position |
Position in Bollinger Band (0=lower, 1=upper) | 0-1 |
macd_hist_norm |
MACD histogram / Close | ratio |
consecutive_up_days |
Count of consecutive up-close days | days |
distance_from_20d_high |
(Close - 20d High) / 20d High | % |
prev_day_return |
Previous day's return | % |
Report Output
Section 1: Feature Mean Comparison (Winners vs Losers)
Table showing each factor's mean for winning vs losing trades, the delta, and a diagnostic interpretation. Example:
| Gap Open % | +3.2% | +1.1% | +2.1% | Winners enter on stronger gaps |
| Volume Surge | 2.8x | 1.4x | +1.4x | Volume confirmation helps |
Section 2: What-If Filter Analysis
Simulates applying each filter rule to the full trade set and measures:
- How many losers would be avoided
- How many winners would be accidentally killed
- Net impact on total portfolio ROI
- Verdict: Shield (improves total P&L), Toxic (kills outlier wins), or Marginal
Filters tested include: gap > 2%, volume > 2x, below MA20, RSI > 70, BB > 0.95, consecutive up > 3, near 20d high, negative intraday return.
Combined filter: Stacks all "Shield" filters and reports the combined effect.
Section 3: Winner Entry Profile
Statistical percentile ranges (25th-75th) for each factor among winning trades. Defines the "ideal entry environment" envelope.
Output Files
<name>_features.csv— Full feature matrix for all tradesfeature_diagnosis.md— Complete markdown report
Caching
YFinance data is cached per-ticker in yfinance_cache/ alongside the orders CSV. Subsequent runs on the same data skip downloads entirely.
Dependencies
pip3 install pandas numpy yfinance
Rules
- Do not modify files in
yfinance_cache/manually. The cache uses date-range coverage checks. Corrupted cache files will cause silent data gaps in indicator calculations. - Do not change indicator parameters (RSI period, Bollinger window, etc.) without understanding that all 12 factors are calibrated together. Changing one shifts the entire winner/loser comparison baseline.
- What-If verdicts ("Shield" vs "Toxic") are based on total portfolio ROI impact, not win rate. A filter that improves win rate but kills outlier wins is marked "Toxic" because total P&L decreases. Do not override this logic.
- Internet access is required on first run for each ticker. Subsequent runs use cached data. If you run in an offline environment, pre-populate the cache directory.
- Minimum 20 closed trades required for statistically meaningful feature comparison. With fewer trades, the report will still generate but conclusions are unreliable.