Published on 07 June 2025 | 👁️ 125
In our previous articles, we walked through converting a TradingView indicator into a working strategy, optimizing it with filters, and improving its performance using computer algorithms.
But even the best backtest results don’t guarantee real-world profitability.
In this final part of the series, we’ll show how we ensure our strategies are robust enough for live trading, and how we prepare for real deployment — including how we plan to track and share results publicly.
When we optimize strategies using filters or machine learning (like Genetic Algorithms), there’s always a risk of overfitting — creating a strategy that performs well only in the backtested dataset, but fails when the market changes.
We don’t want fancy stats. We want strategies that continue to perform live, under real market stress.
That’s why we put every strategy through a robustness testing pipeline.
Here’s a checklist we use before approving any strategy for live deployment:
A strategy should be conceptually sound — not overly complex. If it goes into drawdown, you’ll only stick to it if you understand why it works. Complexity often hides fragility.
We test the strategy on out-of-sample data — periods that were not part of the optimization window. This shows whether the strategy generalizes well to different market conditions and doesn’t just "memorize" the past.
We simulate real-world frictions and edge cases:
What happens if you remove the top 5% best trades?
What if spread widens or slippage increases?
How does the strategy behave in low-volume periods?
These stress tests reveal hidden dependencies that could hurt live performance.
We run thousands of trade reshufflings to model potential equity curves. This helps estimate:
Risk of Ruin
Variability of returns
Worst-case drawdowns
Confidence intervals
If your backtest result is just one lucky path, Monte Carlo will expose it.
We test how performance changes when input parameters are varied slightly:
Is the edge stable around those values?
Or is performance dependent on one exact setting?
Stable strategies are not hypersensitive to minor tweaks.
We check the strategy’s performance across different market regimes:
Trending vs. ranging
High vs. low volatility
Bull vs. bear conditions
A good strategy should adapt or survive across phases — not only shine in one.
Only if a strategy:
Has sound logic
Performs well out-of-sample
Handles stress scenarios
Shows stable performance across Monte Carlo simulations
Is not sensitive to tiny parameter changes
And is consistent across different market regimes
… do we consider it robust and ready for deployment.
Even the best single strategy may have losing streaks.
That’s why we believe in building a portfolio of uncorrelated strategies — each tested independently, but designed to complement each other.
Diversification across:
Instruments
Timeframes
Signal types
Strategy logic
… reduces overall volatility and smooths equity growth.
We’ve selected one such strategy — built from the HalfTrend logic, enhanced with filters, and rigorously tested for robustness.
📍 Deployment: NIFTY 5M timeframe
📈 Tracking: All trades and performance will be published weekly
We’re also considering launching a Telegram channel to publish:
Live trade signals
Entry/exit updates
Weekly performance reports
… so our followers can track our systems in real-time.
Finding an edge is just the beginning. The real work is in testing, filtering, validating, and managing that edge over the long term.
We hope this series helped you understand how professional strategies are built — and how discipline and process matter more than prediction.
📣 Stay tuned as we go live, and follow us for:
Trade results
Performance breakdowns
New strategy releases
Automation updates
👉 If you're serious about building sustainable trading systems — this is just the beginning.