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-forensics for 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 trades
feature_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.