backtest-expert

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Expert guidance for systematic backtesting of trading strategies on Indian markets (NSE/BSE). Use when developing strategies, testing robustness, avoiding overfitting, or validating trading ideas.

Testing & QA 49 stars 41 forks Updated 1 weeks ago MIT

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Skill Content

# 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 exe...

Details

Author
ajeeshworkspace
Repository
ajeeshworkspace/indian-trading-skills
Created
4 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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