Read this first. This is an educational reference written from a software point of view. We build trading systems; we are not a SEBI-registered investment adviser and none of this is advice to trade anything. What follows are seven strategy patterns clients most often ask us to implement, and what each one costs in engineering effort. Whether any of them makes money depends on your implementation, your capital and the market — not on the code.
1. Opening Range Breakout
The one almost everyone starts with. Capture the high and low of the first fifteen minutes, from 9:15 to 9:30. If price breaks above that high, go long; below the low, go short. The opposite end of the range becomes the stop.
From a coding view it is the simplest thing on this list — two variables, one comparison, one order. Which is exactly why it is a good first build: you spend your time learning order handling rather than fighting the strategy logic.
It behaves very differently on trending days versus range-bound ones, and any honest backtest has to show both.
2. Short straddle on BankNifty
Sell an at-the-money call and an at-the-money put together, typically near expiry, on the view that the index stays within a range and both premiums decay.
Engineering-wise this is harder than it looks. You now have two legs whose state has to stay synchronised. If one leg fills and the other is rejected, your code has to notice within seconds and decide what to do — because a half-filled straddle is a directional position you never intended to take.
Theoretical risk on an unhedged short straddle is unbounded. Most people who build this end up hedging it, which turns it into the next item.
3. Iron condor
Four legs: sell a call and a put near the money, buy a further out-of-the-money call and put as protection. The bought legs cap the loss, which is the entire point.
More legs means more that can go wrong at entry, so the code needs proper order-state tracking and a rollback path if a leg fails. But because maximum loss is defined by the structure itself, the risk logic is far easier to reason about than the naked version.
This is the structure we get asked to build most often for weekly BankNifty expiries.
4. Mean reversion on RSI
Buy when RSI drops below 30, sell when it rises above 70, on the assumption that stretched readings snap back.
Straightforward to code — Pandas computes RSI in a line. The difficulty is elsewhere: in a strong trend, RSI can sit below 30 for days while price keeps falling, and a naive implementation keeps buying into it. Any serious version needs a trend filter and a maximum position count.
5. VWAP pullback
When a stock trading above VWAP falls back to touch it, treat that as a support test and enter long, with a stop a fraction below the line.
Easy to build, and pleasant to debug because VWAP is a single reference line you can plot and eyeball against your logs. Just be careful that you are computing VWAP the same way your charting platform does, or your backtest and your screen will disagree and you will waste an evening.
6. Pair trading
Take two correlated instruments — HDFC Bank and ICICI Bank is the standard example. When the spread between them widens abnormally, short the stronger and go long the weaker, then close both when the spread normalises.
This is the most demanding build here. You need historical correlation analysis, a statistically defensible definition of "abnormal", and simultaneous execution across two instruments. The upside is a market-neutral structure, which is why quant-minded clients keep asking for it.
7. Momentum with a volume filter
A move of around one percent accompanied by volume roughly three times the average is treated as confirmation, and the position rides with a trailing stop.
Medium difficulty. The trailing stop is the part that takes the time — deciding whether it trails on price, on ATR, or on a moving average changes the behaviour completely, and it is worth backtesting all three rather than guessing.
Comparison — by build effort, not by returns
This table compares coding difficulty and structure. It says nothing about performance, and it should not be read as a ranking of what works.
| Strategy | Coding difficulty | Number of legs | Common use-case |
|---|---|---|---|
| Opening Range Breakout | Easy | Single | Intraday breakout study |
| Short Straddle | Hard | Two-leg | Volatility study |
| Iron Condor | Medium | Four-leg | Defined-risk study |
| Mean Reversion | Medium | Single | Indicator-based study |
| VWAP Pullback | Easy | Single | Reference-line study |
| Pair Trading | Hard | Two-leg | Statistical study |
| Momentum | Medium | Single | Volume-trend study |
Backtest before anything goes live
Whichever of these you pick, do not run it with real money until it has been tested against at least two years of historical data, with brokerage and slippage included. Leaving costs out is the single most common way a backtest flatters a strategy that loses money in practice.
We code and backtest custom strategies for clients. What we deliver is a system that executes your rules reliably. The rules, the capital and the outcome remain yours.
FAQs
Which strategy is easiest to code first?
Opening Range Breakout. It is two variables and one comparison, which means your first build teaches you order handling and error recovery rather than strategy complexity.
How are options selling strategies structured in code?
Multi-leg strategies need order-state tracking so that a partially filled position is detected immediately, plus a rollback path if a leg is rejected. A hedged structure like an iron condor is far easier to reason about than a naked short straddle. No one can guarantee performance — that is market-driven.
Do you provide trading strategies or tips?
No. We build software. We are not a SEBI-registered investment adviser and we do not supply strategies, signals or recommendations. Clients bring their own logic and we implement it.
Why does my backtest look better than live trading?
Usually one of three reasons: brokerage and slippage were left out, the parameters were tuned until the past looked good, or the fill assumptions were optimistic. Add real costs first — that alone explains most of the gap.
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