Strategy Lab
I code your strategy. Then I test it — properly.
Bring the rules. I turn them into Python, run a realistic backtest, and give you a clear performance picture — so you stop guessing and start deciding with evidence.
What this service is
Not signals. Not “I trade for you.” A focused build-and-test engagement on your idea.
Coded rules
Your strategy written as explicit, testable Python — no ambiguous “buy when it looks strong”.
Honest backtest
Fees, slippage, sizing and walk-forward style checks — not a curve-fit fantasy equity line.
Performance report
Returns, drawdowns, Sharpe/Sortino-style metrics, trade stats and where it breaks.
Clear next step
Keep it, kill it, or fix specific weaknesses — with reasons you can act on.
How it works
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1
Brief
You send the rules (markets, timeframe, entries/exits, sizing). We confirm scope and package.
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2
Code
I implement the strategy in Python with clean, readable research code you keep.
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3
Test
I run the backtest with realistic costs and validation — not just an in-sample chart.
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4
Report
You get the code, plots and a written verdict on what the numbers actually say.
Packages
Pick the depth of coding and validation you need. Prices are fixed-scope starting points — complex multi-asset books may need a custom quote.
One clear idea
Strategy sprint
$249 fixed engagement
You describe the rules. I code them in Python, run an honest backtest with costs, and send you a written performance report — so you know what the idea actually did historically.
- Rules intake (async brief or short call)
- Python implementation of your strategy
- Backtest with fees, slippage and realistic sizing
- Equity curve, drawdowns, metrics & walk-forward check
- Written report + source code you can keep
Serious go/no-go
Full validation
$449 fixed engagement
Coding plus a research-grade validation pass: biases, overfitting checks, regime splits and capacity notes — so you leave with a clearer idea of whether the edge is real.
- Everything in Strategy sprint
- Lookahead / survivorship / leakage review
- Walk-forward or purged validation
- Parameter sensitivity & robustness notes
- 1 follow-up call to walk through results
Ready to operationalize
Research → live stack
$799 fixed engagement
I turn your strategy into a maintainable research codebase: clean data pipeline, backtest engine, risk sizing hooks and a deployment checklist — not just a one-off notebook.
- Everything in Full validation
- Modular Python research package
- Config-driven parameters & reproducibility
- Basic monitoring / paper-trade checklist
- Handoff session on how to extend it
FAQ
Do you invent the strategy for me?
No. You bring the idea and rules. I code and test what you specify — so the results are about your edge, not mine.
What do I need to provide?
Markets, timeframe, entry/exit logic, filters, and how you size positions. Notebooks, screenshots or a plain-English ruleset all work — the clearer the rules, the cleaner the test.
Will this guarantee future profits?
No. A backtest clarifies historical behaviour under stated assumptions. Markets change; past performance is not a promise.
Who owns the code?
You keep the implementation and report for your package. I don’t trade your account or sell your rules.
How is this different from coaching?
Coaching is guidance on your work. Strategy Lab is a delivery engagement: I write the code and run the tests, then hand you the results.
How long does it take?
Typical turnaround is 1–3 weeks depending on package complexity and how complete your rules are. I’ll confirm timing before we start.
Request a strategy test
Describe the rules as precisely as you can. Ambiguous strategies get ambiguous tests.