Most traders in India pick up a strategy from a YouTube video or a Telegram group, try it with real money, and find out three weeks later that it doesn't work. A backtest answers that question in minutes instead of…
Most traders in India pick up a strategy from a YouTube video or a Telegram group, try it with real money, and find out three weeks later that it doesn't work. A backtest answers that question in minutes instead of weeks, and without risking a rupee.
The problem is that most backtesting tools assume you can write Python or Pine Script. Welth Lab removes that barrier. It is a no-code backtesting engine for NIFTY 50, Bank NIFTY and 500+ NSE stocks, with over 10 years of historical data. This guide walks through the complete pipeline, step by step, and shows you how to read the results like a quant.
What you'll learn
- How the 5-stage Welth Lab pipeline works
- How to set entries, exits, stop-losses and position size
- What Sharpe, Sortino, Calmar and max drawdown actually tell you
- Why Monte Carlo simulation matters, and the mistakes that fake a good backtest
What is backtesting, and why should you do it?
Backtesting means applying a fixed set of trading rules to historical price data to see how those rules would have performed. If your rule is "buy when RSI drops below 30 and the price is above the 200-day EMA," a backtest finds every time that happened in the past, simulates the trade, and records the result.
It won't predict the future, but it does three valuable things: it kills bad ideas cheaply, it tells you how deep the losing streaks get, and it shows whether your edge survives brokerage and slippage.
Inside Welth Lab: the 5-stage simulation pipeline
Welth Lab organises a backtest as a pipeline. Each stage is a card you configure in order, and every card starts with sensible defaults, so you can run a first test immediately and refine from there.
| Stage | Question it answers | Default |
|---|---|---|
| 1. Input | What to test, and over what period | 1 instrument, 5Y, Daily |
| 2. Strategy | When to buy and sell | 4 indicators, 4 conditions |
| 3. Risk Management | When to cut a loss and take a win | Balanced, 2× ATR stop |
| 4. Position Sizing | How much to buy, and what it costs | Risk per trade, 5 max open |
| 5. Execution | Review and run | ~36 simulations |
Step 1: Input — choose what to test
Pick the instrument (NIFTY 50, Bank NIFTY or any of 500+ NSE stocks), the lookback period and the timeframe. The default is five years of daily candles, which is a good starting point: long enough to include a bull phase, a correction and a sideways market.
Tip: test the same rules on at least three different stocks or indices. A strategy that only works on one symbol is usually a coincidence, not an edge.
Step 2: Strategy — define entry and exit rules
This is where you build your logic from indicators such as RSI, MACD, Bollinger Bands, SMA/EMA crossovers, Stochastic, ATR, OBV and VWAP. Each condition is a plain-language rule, for example "EMA 20 crosses above EMA 50" or "RSI below 35". The default template uses four indicators and four conditions so you can see how a complete rule set is structured.
Tip: combine one trend filter (like a 200 EMA) with one timing trigger (like RSI or MACD). Stacking five oscillators that all measure momentum adds complexity without adding information.
Step 3: Risk Management — set stop-loss and target
Here you decide how each trade ends. The default "Balanced" profile places the stop-loss at 2× ATR (Average True Range), which means the stop automatically widens in volatile markets and tightens in quiet ones. That is far more robust than a fixed 1% or 2% stop, because a 2% move in a calm large-cap and in a volatile mid-cap are very different events.
Step 4: Position Sizing — decide how much to risk
Position sizing is the most ignored part of retail trading, and often the most important. Welth Lab sizes each trade by risk per trade: you choose what share of capital you're willing to lose if the stop is hit, and the engine works out the quantity. The default also caps you at five open positions, so one bad week can't wipe out the account. Trading costs are applied here too, so your results reflect what you'd actually keep.
Step 5: Execution — review and run
The final card summarises the whole pipeline before you commit. Click Review and run and Welth Lab executes the backtest along with a batch of simulations (around 36 with default settings) to stress-test the result rather than relying on a single historical path.
How to read your backtest results
Once the run finishes, you get candlestick charts with every entry and exit plotted, indicator panels, a full trade log and a set of quant metrics. Here is what the key numbers mean:
| Metric | What it tells you | Rough guide |
|---|---|---|
| Sharpe ratio | Return per unit of total volatility | >1 decent, >2 strong |
| Sortino ratio | Like Sharpe, but only penalises downside swings | Higher than Sharpe is normal |
| Calmar ratio | Annual return divided by max drawdown | >1 is healthy |
| Max drawdown | Largest peak-to-trough fall in equity | Could you sit through it? |
| Profit factor | Gross profit divided by gross loss | >1.5 is solid |
| Win rate | Share of trades that made money | Read with avg win/loss |
Don't judge a strategy on win rate alone. A system that wins 35% of the time can be very profitable if its average win is three times its average loss, while a 70% win-rate system can lose money if a few large losses erase many small gains.
Why Monte Carlo simulation matters
A single backtest is one version of history. Monte Carlo simulation reshuffles and resamples your trades to produce many alternative equity curves. If most of those curves stay profitable and the worst-case drawdown is still tolerable, your result is likely more than luck. If a small change in trade order turns profit into loss, the strategy is fragile.
Watch the full Welth Lab walkthrough
Prefer to see it in action? This video builds and runs a complete pipeline from scratch:
▶ Watch the Welth Lab walkthrough on YouTube
4 backtesting mistakes to avoid
- Overfitting. Tweaking parameters until the curve looks perfect. If RSI 31 works but RSI 30 and 32 don't, you've fitted noise.
- Too few trades. Twelve trades in five years is not a statistically meaningful sample. Aim for at least 30 to 50.
- Ignoring costs. Brokerage, STT and slippage can turn a thin edge negative, especially on intraday timeframes.
- Testing only one market phase. A strategy built only on 2020 to 2021 data has never seen a real bear market.
Frequently asked questions
Do I need to know coding to use Welth Lab?
No. Every stage is configured with menus and presets. You can run your first backtest using the defaults and adjust from there.
Which instruments can I backtest?
NIFTY 50, Bank NIFTY and 500+ NSE-listed stocks, with more than 10 years of historical OHLCV data.
Does a profitable backtest guarantee future profits?
No. It shows how your rules behaved in the past. Use it to filter out weak ideas and understand risk, then paper-trade before committing capital.
Test your first strategy today
Run a backtest on NIFTY or your favourite NSE stock with the default pipeline, then change one stage at a time to see what really drives performance.
Open Welth LabDisclaimer: WelthWest is an analytics and education platform, not a SEBI-registered investment adviser or broker. Backtest results are simulated and are not indicative of future returns. This article is for educational purposes only and is not investment advice.