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I will backtest your trading strategy in python with honest statistics
Finance Broski
About this gig
I backtest trading strategies the way professional quant desks do - and I'll tell you the truth about yours, even when the truth is no.
I recently found a look-ahead bug in my OWN systematic book that had faked an entire "edge" - that's the rigor I bring to your strategy.
What you get:
Survivorship-free testing (delisted stocks included where relevant)
Realistic costs: commissions, slippage, spread - not fantasy fills
Honest statistics: Sharpe, max drawdown, hit rate, turnover
Benchmark comparison so you know if the strategy beats doing nothing
Premium tier: walk-forward analysis and overfitting checks - the tests that separate real edges from curve-fit illusions
If your edge is real, you'll know. If it's not, you'll know that too - before you risk capital on it.
Get to know Finance Broski
Finance Broski
AI Automation and Python Developer, Data Pipelines, Trading Systems
- FromIndia
- Member sinceJun 2026
- Avg. response time1 hour
Languages
Hindi, English
I build Python automations, AI integrations (GPT, Claude, Gemini APIs), and data pipelines that run daily without you thinking about them. Every deliverable ships with verification built in: row counts, integrity checks, automated tests. My standard comes from quantitative finance, where silent data errors cost real money. Specialty niche: trading systems - honest backtesting, broker API integration. Code samples: github.com/Finance-broski - six public repos, including an execution layer with 11 passing tests. Tell me the task you do manually; I will tell you if it is automatable.
FAQ
Can you guarantee the strategy will be profitable?
No - and nobody honest can. What I guarantee is an honest test: realistic costs, no curve-fitting, and a clear verdict. I recently rejected my own strategy when it failed its benchmark despite a Sharpe above 1. You are buying the truth, not a sales pitch.
What do you need from me to start?
Your strategy rules written as precisely as you can (entries, exits, position sizing), the market and instruments, and your data source if you have one. If the rules are ambiguous I will ask before coding - ambiguity is where backtests go wrong.
Which markets and platforms do you cover?
Indian equities (NSE) with my own survivorship-free 16-year dataset, US stocks, forex, and crypto with standard data sources. Output is a Python backtest with full trade log - platform-independent.
How do I know the results aren't overfit?
That is the core of what I sell. Standard and Premium tiers include walk-forward testing; Premium adds parameter sweeps and out-of-sample validation. If your strategy only works for one magic parameter value, I will show you that - before the market does.
Can you turn the strategy into a live bot or EA afterward?
Yes, as a separate project - I build execution systems too (see my profile). I deliberately keep testing and deployment separate: you should only automate a strategy after it has survived honest testing.
Is my strategy kept confidential?
Completely. Your rules are never reused, shared, or traded by me. I run my own systematic research - I have no interest in your edge, only in testing it properly.
What if the backtest shows no edge?
Then I tell you exactly that, with the numbers and the reasons. A 50 dollar test that stops you deploying a losing strategy is the cheapest insurance in trading. Roughly half of honest backtests end this way - knowing beats hoping.
What timeframes and data can you handle?
Daily and intraday down to 1-minute bars, depending on data availability for your market. Tick data and order-book simulation are out of scope for these packages - I will say so upfront if your strategy needs them.
