You bring the strategy idea and the exchange account. I build the bot: signal logic, risk management, backtest report, paper-trading validation, and source code you own outright — delivered so you can read every line, not just run it.
Pick the tier that matches your strategy's complexity. All tiers include source code, a written backtest report, and a paper-trading validation window before anything touches real funds.
One exchange, one strategy, clean and simple.
A production-shaped bot with real risk controls.
Multi-strategy, multi-exchange, hands-on deployment.
Below is actual output from this build's backtesting engine — three strategies run against a reproducible synthetic price series (see disclaimer). It's here to prove the engine is correct: sizing, stop-loss/take-profit, and drawdown tracking all fire exactly as configured. A real engagement backtests against your exchange's actual historical data, not synthetic data.
| Strategy | Return | Win rate | Trades | Max DD |
|---|---|---|---|---|
| EMA Crossover | -1.95% | 21.1% | 19 | 2.95% |
| RSI Reversion | -3.15% | 18.2% | 11 | 3.81% |
| Grid | -6.02% | 32.4% | 34 | 7.42% |
Why these numbers are negative and that's fine: the sample data is a reproducible synthetic random walk, not real market history — it has no genuine edge to find. These runs demonstrate that the backtester, risk manager, and each strategy's signal logic behave exactly as configured, which is what you're actually paying an engineer for. No bot — mine or anyone else's — guarantees profit, and I won't tell you otherwise to close a sale.
# src/strategies/base.py — the interface every strategy implements class Strategy: min_lookback: int = 1 def generate_signal(self, ohlcv: DataFrame) -> Signal: """ohlcv: timestamp, open, high, low, close, volume""" raise NotImplementedError # your strategy becomes one new file here — # EMA crossover, RSI reversion, and grid trading # already ship as working references.
You describe the strategy/indicators; I confirm exchange, pair, and risk tolerance.
Strategy logic + risk manager coded against the shared engine. 3–5 business days.
Run against your exchange's real historical data. You get the full report and trade log.
Live market data, simulated fills. You watch it work before a cent is at risk.
You flip the switch — on your exchange account, your API keys, your call.
Reply with your strategy idea, target exchange, and budget — I'll confirm scope and turnaround before anything is billed.
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Say so in the form and I'll send a payment link — no card details are ever collected on this page.
Only for the final live-mode handover, and only into your own .env file on your own machine or server — never sent to me, never stored anywhere I control. Backtesting and paper trading need no keys at all.
No, and anyone who tells you yes is selling something else. What I guarantee is that the code does exactly what the backtest and paper-trading results show it does — no silent logic bugs, no hidden risk exposure.
Any of the ~100 exchanges supported by the ccxt library — Binance, Kraken, Coinbase, Bybit, KuCoin, and most others. Exchange-specific quirks (rate limits, order types) are handled per build.
Full source code (Python), a written backtest report, setup instructions, and — on Standard/Pro — Docker files for unattended VPS deployment. You own the code outright.
3–5 business days for Starter/Standard once scope is confirmed; Pro builds with multi-exchange or webhook integration typically run 5–8 business days.