What is Quantitative Trading?
Quantitative trading encodes signals in data-driven rules with minimal discretion.
Formula
Risk per trade: 1% of account = $500
Indian market context (NSE)
Reference levels: Nifty 50 at 24,300, Reliance Industries at ₹1,300, Bank Nifty futures at 55,000 (lot size 30). Examples below show how Quantitative Trading shows up on Indian index, equity, and futures books — update to live quotes in your journal.
Nifty 50 perspective
Quantitative Trading on Nifty (24,300): backtest includes 9:15 liquidity and expiry-day behaviour; edge on index may vanish outside 10:00–14:30 window.
Reliance Industries perspective
Quantitative Trading on Reliance (₹1,300): liquidity is deep but event gaps dominate — strategy rules need explicit earnings blackout weeks.
Bank Nifty futures perspective
Quantitative Trading on Bank Nifty futures (55,000): high beta suits shorter holds; overnight quantitative trading must state NRML risk and gap plan in writing.
How to validate
- Validate Quantitative Trading only after costs — gross win rate can hide negative expectancy.
- Use walk-forward windows (e.g. last 60 / prior 60 trades) for stability.
- Retire or refactor the tag if Quantitative Trading expectancy turns negative with 50+ trades.
- Ensure no overlapping tags duplicate the same trades.
How to track in TradeLyser
- Define Quantitative Trading in Strategy Board with entry/exit/skip criteria.
- Enforce single-tag discipline — no secondary discretionary entries.
- Review expectancy, win rate, and avg R monthly on the tag only.
- Archive tag version when rules change; do not blend old and new trades.
Best practices
- One playbook page per Quantitative Trading strategy with non-negotiable rules.
- Paper trade rule changes for two weeks before live size.
- Track costs explicitly on high-frequency Quantitative Trading variants.
- Compare versioned tags after each rule amendment.
Common pitfalls
- Adding discretionary trades under the Quantitative Trading tag.
- Scaling up after one lucky week of Quantitative Trading results.
- Ignoring brokerage drag on high-frequency variants.
- Retiring a tag without exporting final statistics.
How to use this in TradeLyser
Store parameter version on each trade; backtest and forward test before size.
Related terms
Algorithmic trading automates entries, exits, and sizing via code or platform rules, reducing discretion at execution.
Backtesting applies strategy rules to past data to estimate performance — subject to bias.
Expectancy answers whether your edge pays each time you repeat the setup. Positive expectancy means the system earns over many trades; negative expectancy means it bleeds even with a high win rate.
Trading edge is a statistical or structural advantage that produces positive expectancy over many trades.
FAQ
Quant without coding?
Spreadsheet rules count — version them.
Overfit Indian small sample?
Walk-forward and out-of-sample months required.
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