Discretionary trading is exhausting and error-prone. A mechanical strategy is a set of if-then rules — no judgment calls, no emotion. The rules either work or they don't; you know it from the data. This is how you turn a good idea into a repeatable system.
Discretionary trading: you look at the market, form a view, place a trade based on judgment.
Mechanical trading: fixed rules define entries, exits, sizing.
Most professional trading is mechanical. Even “discretionary” traders often use mechanical filters.
If any is vague, you’ll waffle in the moment. If all are specific, execution becomes mechanical.
Entry conditions (all must be true, checked every Monday at 10 AM):
Trade construction:
Stop-loss:
Take-profit:
Time exit:
Skip rules:
Every one of these is precise. There’s no “if I feel the market is…” — either the rule triggers or it doesn’t.
Step 1: Hypothesis
Write down what you think works. “Selling weekly iron condors on quiet Nifty weeks generates positive returns.”
Step 2: Formalize as rules
Convert the hypothesis into precise entry/exit/sizing rules as above.
Step 3: Backtest
Test the rules against historical data. Look at:
Step 4: Reality check
Step 5: Walk-forward test
Split your data:
Step 6: Paper trade
Run the strategy live but without real money for 1-3 months. Check:
Step 7: Live trading with minimum size
Start with the smallest possible position. Scale up over months as the strategy proves itself.
Options for Indian retail:
Free / low-cost:
Coding required:
Professional:
For most retail, start with Streak or Sensibull to validate the concept, then upgrade to Python if you get serious.
1. Survivorship bias: using only stocks that still exist. Fixed by using historical constituent lists.
2. Lookahead bias: using data that wouldn’t have been available at trade time. Common with EOD data used as intraday signals.
3. Overfitting: tuning parameters until backtest looks great. Almost always fails live.
4. Ignoring transaction costs: brokerage + GST + STT + slippage = ~₹50-200 per trade round-trip. Small strategies get eaten alive.
5. Ignoring liquidity: strategy trades illiquid strikes; real fills are far worse than backtest.
6. Sample size too small: 20 trades of data is nothing. Aim for 100+ trades before believing anything.
Most backtested “winning” strategies fail in live trading. Overfitting, transaction cost underestimation, and regime changes destroy them. A profitable backtest is necessary but not sufficient — walk-forward and paper testing are non-negotiable before real capital.
Once your strategy is rules-based, consider automating:
Streak (by Zerodha): point-and-click strategy builder + auto-execution. Free with Zerodha account.
Algo trading via API: Zerodha Kite Connect, Angel SmartAPI. Requires Python coding. Approved by SEBI for retail with disclosure.
Semi-automation: get alerts when rules trigger; place trades manually. Best of both worlds if you want speed + control.
Yes if:
No if:
A mechanical strategy is not “set and forget forever.” Review quarterly:
Update rules ONLY based on:
Do NOT update rules based on:
Mechanical trading isn’t about a single perfect strategy. It’s about building a personal library:
Each strategy compounds your knowledge and diversifies your income streams. The best professional traders often have 5-10 mechanical strategies running simultaneously, each contributing modestly.