You will get Audit your trading bot or backtest for overfitting and hidden risk
Project details
You will get a clear verdict on whether your trading bot or backtest can be trusted before you risk real money on it. A backtest that looks great is often the most dangerous kind, because overfitting hides inside good numbers, and an AI-generated bot will faithfully run whatever logic it was handed, including the parts that quietly drain an account.
I find the failure modes that sink AI-built and hand-built systems alike: overfitting, look-ahead bias, broken position sizing, missing stops, and order-handling bugs. You get a written report in plain language, a severity ranking so you fix what matters first, and the specific changes that move the needle. Where the data allows, I run a walk-forward test on out-of-sample periods, so the edge has to prove itself on bars the strategy never saw.
I work in Python, Pine Script, and standard backtest exports, across tastytrade, Interactive Brokers, Kalshi, TradingView, and most broker APIs. Every project starts from your materials and a scope confirmed in writing. I won't promise the strategy will make money, and a clean audit is not a forecast of profit. What you get is the truth about your system before it costs you.
I find the failure modes that sink AI-built and hand-built systems alike: overfitting, look-ahead bias, broken position sizing, missing stops, and order-handling bugs. You get a written report in plain language, a severity ranking so you fix what matters first, and the specific changes that move the needle. Where the data allows, I run a walk-forward test on out-of-sample periods, so the edge has to prove itself on bars the strategy never saw.
I work in Python, Pine Script, and standard backtest exports, across tastytrade, Interactive Brokers, Kalshi, TradingView, and most broker APIs. Every project starts from your materials and a scope confirmed in writing. I won't promise the strategy will make money, and a clean audit is not a forecast of profit. What you get is the truth about your system before it costs you.
AI Development Type
Model Tuning, Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$550
|
Advanced
$1,100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Rush delivery
+$150Frequently asked questions
About Serge
Trading Automation Dev + Strategy Validation | Python, Pine, APIs
Naples, United States - 7:41 pm local time
I build the systems that turn a defined options strategy into a screened, sized, risk-checked order: live broker-API integration, real-time market data, accurate position sizing, and the safety rails that keep automation from doing something dumb.
What I build:
- Options automation against your broker (credit spreads, iron condors, long options), with fill-quality grading and portfolio risk limits enforced in code
- Prediction-market and signal bots (Kalshi, signed-auth APIs, Black-Scholes fair-value pricing, Kelly sizing)
- TradingView indicators and strategies in Pine Script (v5/v6)
- Backtesting and paper-trading engines, so a strategy proves itself before real money is involved
Already built a bot with AI? Have it pressure-tested before it trades real money.
A backtest that looks great is usually the most dangerous kind, because overfitting hides inside good-looking numbers. An AI-generated bot will faithfully run whatever logic it was handed, including the parts that quietly drain an account. I review the strategy and the code for overfitting, look-ahead bias, position-sizing errors, and missing risk limits, then walk-forward test it on out-of-sample data so you find out whether the edge is real before you fund it.
How I work:
Every engagement starts with a short written spec so we agree on scope before I build. I treat your broker credentials and risk limits as load-bearing, not afterthoughts. I won't promise a strategy will be profitable. I build the system correctly and let your edge speak for itself.
Stack: Python, FastAPI, React, REST and WebSocket APIs, OAuth / RSA-PSS signed auth, Pine Script.
If you've got a strategy and a brokerage account and want them wired together properly, send me the details and I'll tell you exactly how I'd approach it.
Steps for completing your project
After purchasing the project, send requirements so Serge can start the project.
Delivery time starts when Serge receives requirements from you.
Serge works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff and scope confirmation
I review what you sent, confirm the scope in writing, and flag anything I still need before starting.
Strategy and code review
I go through the logic and code for look-ahead bias, position-sizing errors, missing risk limits, and order-handling bugs.