backtest-expert

Backtest Expert — Indian Market Strategy Validation

Core Philosophy

"Find strategies that break the least, not profit the most."

A strategy that survives stress testing across multiple market regimes, transaction cost assumptions, and parameter perturbations is far more valuable than one that shows spectacular returns on a single optimized parameter set. Overfitting is the silent killer of trading accounts.

6-Step Backtesting Workflow

Step 1: State the Hypothesis (1 Sentence Edge)

Before writing a single line of code, articulate why the strategy should work in one clear sentence.

Good hypotheses:

• "Stocks that gap up >3% on above-average volume after consolidation tend to continue higher for 2-5 days on NSE."
• "Nifty 50 stocks that revert to their 20-day mean after RSI drops below 30 produce positive expectancy within 5 trading sessions."
• "Selling strangles on Bank Nifty on Wednesday expiry with delta <0.15 captures time decay faster than gamma risk materializes."

Bad hypotheses:

• "This indicator combination looks good on the chart." (no edge articulated)
• "I saw someone on Twitter making money with this." (no reasoning)

Ask yourself:

• What behavioral or structural edge am I exploiting?
• Why would this edge persist? (Structural > Behavioral > Statistical)
• Who is on the other side of this trade, and why are they losing?

Step 2: Codify Rules (No Ambiguity)

Every rule must be binary — a computer must be able to execute it without interpretation.

Rule Categories

| Category | What to Define | Example | |----------|---------------|---------| | Universe | Which stocks/instruments | Nifty 200 constituents, F&O stocks only, market cap >5000 Cr | | Entry | Exact trigger conditions | Close > 20 EMA AND RSI(14) crosses above 40 AND volume > 1.5x 20-day avg | | Exit — Target | Profit-taking rule | Close 3% above entry OR trailing stop of 1.5 ATR | | Exit — Stop | Loss-cutting rule | Close below entry-day low OR 2% fixed stop | | Exit — Time | Maximum holding period | Exit after 10 trading sessions if neither target nor stop hit | | Position Sizing | How much capital per trade | 5% of equity per position, max 10 concurrent positions | | Filters | When NOT to trade | Skip if stock is in F&O ban period, skip 2 days around results |

India-Specific Rules to Consider

• Circuit limits: Stocks hitting upper/lower circuit cannot be exited. Define handling.
• F&O ban period: Stocks crossing 95% MWPL cannot add fresh F&O positions.
• T+1 settlement: Cash equity settles next trading day (changed from T+2 in 2023).
• Pre-open session: 9:00-9:08 AM orders, 9:08-9:15 AM matching. Define if you use pre-open.
• Muhurat trading: Special Diwali session — include or exclude?
• Corporate actions: Adjust for splits, bonuses, dividends, rights issues.

Step 3: Run Initial Backtest

Minimum Requirements

| Parameter | Minimum | Recommended | |-----------|---------|-------------| | Time period | 5 years | 8-10+ years | | Number of trades | 100 | 200+ | | Market regimes covered | 2 (bull + bear) | 4+ (bull, bear, sideways, high-vol) | | Data quality | Adjusted for corporate actions | Survivorship-bias-free universe |

Indian Market Regimes to Cover

| Regime | Period Examples | Characteristics | |--------|----------------|-----------------| | Bull market | 2014-2017, 2020-2021 | Nifty trending up, broad participation | | Bear market | 2008, 2020 (Mar), 2022 (Jun) | Sharp drawdowns, high correlation | | Sideways/Range | 2018-2019, 2023 H1 | Nifty in 10% range, stock-specific moves | | High volatility | 2008, 2020, Budget days | India VIX > 25 | | Low volatility | 2017, 2021 H2 | India VIX < 15 | | Pre/Post Budget | Every Feb 1 | Gap moves, policy-driven sectors | | Election cycle | 2014, 2019, 2024 | Uncertainty then rally pattern | | Monsoon impact | Jun-Sep annually | Agri, FMCG, rural economy impact | | RBI policy shifts | Rate hike/cut cycles | Banking, NBFC, rate-sensitive sectors | | Global crude shock | 2018, 2022 | INR weakness, OMC impact, inflation |

Key Metrics to Record

Returns: CAGR, total return, monthly returns distribution
Risk: Max drawdown, average drawdown, drawdown duration, Calmar ratio
Efficiency: Sharpe ratio (use 6% risk-free for India), Sortino ratio
Trade quality: Win rate, avg win/loss, profit factor, expectancy per trade
Consistency: % profitable months, worst month, longest losing streak

Step 4: Stress Test (Spend 80% of Your Time Here)

This is where most backtests fail — and where the real value lies.

4a. Parameter Sensitivity

Perturb every parameter by +/-20% and check if performance degrades gracefully or collapses.

| Parameter | Base | -20% | -10% | +10% | +20% | Verdict | |-----------|------|------|------|------|------|---------| | EMA period | 20 | 16 | 18 | 22 | 24 | Stable if all profitable | | RSI threshold | 40 | 32 | 36 | 44 | 48 | Fragile if only 40 works | | Stop loss % | 2% | 1.6% | 1.8% | 2.2% | 2.4% | Check drawdown impact |

Rule of thumb: If the strategy only works with exact parameter values, it is overfit. You want a "plateau" of profitability, not a "peak."

4b. Execution Friction (India-Specific Costs)

Apply realistic transaction costs:

| Cost Component | Delivery (CNC) | Intraday (MIS) | F&O | |----------------|----------------|-----------------|-----| | Brokerage | ~₹20/order or 0.03% | ~₹20/order or 0.03% | ~₹20/order | | STT | 0.1% (buy+sell) | 0.025% (sell only) | 0.0125% (sell, options) | | Exchange charges | 0.00345% (NSE) | 0.00345% (NSE) | 0.05% (options) | | GST | 18% on brokerage+exchange | 18% on brokerage+exchange | 18% on brokerage+exchange | | Stamp duty | 0.015% (buy) | 0.003% (buy) | 0.003% (buy) | | SEBI charges | 0.0001% | 0.0001% | 0.0001% | | Slippage | 0.05-0.1% large-cap | 0.1-0.2% mid-cap | 0.1-0.3% options |

Total round-trip cost estimates:

• Delivery large-cap: ~0.3-0.5%
• Intraday large-cap: ~0.1-0.2%
• F&O (options): ~0.15-0.4%
• Small-cap delivery: ~0.5-1.0% (wider spreads)

4c. Time Robustness

• Split data into 3-year rolling windows. Is the strategy profitable in each?
• Check year-by-year returns. Is any single year driving total performance?
• Remove the best month. Is the strategy still positive?

4d. Sample Size Validation

• Minimum 30 trades for any statistical claim (even this is weak)
• 100+ trades: Moderate confidence
• 200+ trades: Good confidence
• Use the t-test: Is average trade return significantly different from zero?

Step 5: Out-of-Sample Validation (Walk-Forward Analysis)

Never skip this step.

Walk-Forward Method for Indian Markets

1. In-sample period: Train on 5 years of data (e.g., 2015-2019)
2. Out-of-sample period: Test on next 1-2 years (e.g., 2020-2021)
3. Roll forward: Move window, retrain on 2016-2020, test on 2021-2022
4. Combine: Aggregate all out-of-sample periods for true performance estimate

Walk-Forward Efficiency (WFE):

WFE = Out-of-Sample Return / In-Sample Return

• WFE > 50%: Good — strategy generalizes
• WFE 30-50%: Acceptable — some overfitting present
• WFE < 30%: Poor — likely overfit

Paper Trading Validation

Before deploying capital, paper trade for at least:

• 30 trades minimum
• 2 months minimum
• Cover at least one volatile period (expiry week, results season, RBI policy)

Step 6: Evaluate Results (Deploy / Refine / Abandon)

Use the evaluation script to get an objective score:

python3 evaluate_backtest.py \
  --total-trades 150 \
  --win-rate 62 \
  --avg-win-pct 1.8 \
  --avg-loss-pct 1.2 \
  --max-drawdown-pct 15 \
  --years-tested 8 \
  --num-parameters 3 \
  --slippage-tested

Decision Framework

| Score | Verdict | Action | |-------|---------|--------| | 80-100 | Deploy | Size small initially