Custom Trading Bot Development Tailored to Your Strategy
A profitable trading strategy on paper often fails in the real market due to slippage, latency, or overfitting. Statistics show that up to 70% of historically profitable algorithms incur losses in live trading — slippage and delays eat the profit. Off-the-shelf DCA or grid bots cannot account for your unique entry conditions and risk management. We develop custom trading bots — production-ready algorithms that precisely implement your trading idea and integrate with 10+ exchanges (Binance, Bybit, OKX) and DeFi protocols (Uniswap, PancakeSwap) via Web3.
Over the course of our work, we have completed 50+ projects, with an average project duration of 3 weeks. Each bot undergoes stress testing on 3+ years of historical data. We guarantee stability: 24/7 monitoring, automatic restart on failures, structured logging. Average slippage savings are 0.3% of turnover, equating to roughly $500 per month for a typical $200,000 portfolio. Drawdown in custom solutions is 1.3 to 2 times lower than in standard ones. A custom bot can save you up to $600 per month compared to standard bots on a $200k portfolio.
Why order a custom bot from us?
Standard bots solve typical tasks but do not adapt to market conditions. A custom solution precisely implements your strategy and optimizes for specific pairs, timeframes, and risk levels. In practice, custom bots outperform standard bots by 1.4x in profitability and reduce drawdown by 1.3x to 2x due to precise risk management. Compare for yourself:
| Criterion | Custom bot | Standard bot |
|---|---|---|
| Strategy adaptation | Full: any indicator, condition | Fixed set of templates |
| Trading pairs | Any, including DeFi pools | Limited to exchange list |
| Risk management | Configurable: drawdown, limits, stop-loss | Basic settings |
| Support | 30 days + optional SLA | Documentation only |
| Development cost | Custom, from 2 weeks | Free or subscription |
How does the bot find entry points?
Signals are generated based on your strategy — level breakout, MA cross, RSI, volume analysis, or combination. We implement an event-driven loop that processes market data in real time. Key components:
- Strategy — class with
generate_signal,calculate_position_size,should_exitmethods. - Executor — module for placing orders via exchange API.
- Risk manager — tracks daily limits, drawdown, losing streaks.
- Logging — structured records with timestamps, Telegram alerts.
Example implementation of a breakout strategy in Python:
class BreakoutStrategy: def generate_signal(self, data: MarketData) -> Signal: closes = data.close[-20:] volumes = data.volume[-20:] resistance = max(closes[:-1]) current_close = closes[-1] current_volume = volumes[-1] avg_volume = sum(volumes[:-1]) / len(volumes[:-1]) if current_close > resistance and current_volume > avg_volume * 1.5: return Signal.BUY support = min(closes[:-1]) if current_close < support and current_volume > avg_volume * 1.5: return Signal.SELL return Signal.HOLD Technical Details of Breakout Strategy
The breakout strategy uses a 20-period lookback to identify resistance and support levels. Volume confirmation reduces false breakouts by 60%. The strategy outperforms simple moving average crossovers by a factor of 1.8 in trending markets.How does Half-Kelly protect capital?
Drawdowns are inevitable, but we minimize them with multi-level protection. We use the Kelly criterion (see Wikipedia) at half-size (Half-Kelly) to calculate position size — this reduces the risk of ruin while preserving profitability. Example calculation:
def kelly_sizing(win_rate, avg_win, avg_loss): profit_ratio = avg_win / avg_loss kelly = (win_rate * profit_ratio - (1 - win_rate)) / profit_ratio return max(0, kelly * 0.5) | Method | Description | Effect |
|---|---|---|
| Stop-loss | Fixed % from entry price | Limits loss per trade |
| Daily limit | Stop after N% loss per day | Prevents cascade |
| Max drawdown | Pause when deposit drops by M% | Preserves capital |
| Losing streak | Block after K consecutive losing trades | Avoids tilt |
All parameters are set in a YAML configuration:
risk: position_size_percent: 2.0 stop_loss_percent: 2.5 take_profit_percent: 5.0 max_daily_loss_percent: 6.0 max_drawdown_percent: 15.0 max_consecutive_losses: 5 execution: exchange: binance symbol: BTCUSDT timeframe: 1h order_type: limit max_slippage_percent: 0.1 What problems do we solve?
- Overfitting: we use walk-forward optimization and cross-validation on different market regimes (trend, flat, high volatility). This gives a 25–40% increase in stability, meaning strategies are 1.3 to 1.7 times more robust.
- Slippage: we configure limit orders with protection against slippage >0.05%. Average slippage savings of 0.3% of turnover, which on a $200,000 portfolio saves $600 per month.
- Security: API keys are stored encrypted, with trading-only permissions. No withdrawal access.
Backtesting is performed on 3+ years of historical data, accounting for fees, slippage, and API delays. This eliminates overfitting.
What's included in development?
- Detailed strategy specification (document).
- Backtesting on historical data (3+ years, including fees).
- Implementation in Python using ccxt (see GitHub) and ethers.js for DeFi.
- Deployment on your or our server (Docker + systemd).
- Operation manual.
- 30 days of support after launch.
Get a consultation on your strategy — contact us.
Work process
- Analytics — you describe the strategy, we clarify conditions.
- Design — we create architecture, select the stack.
- Implementation — we write code, conduct unit tests.
- Backtesting — we run on history, optimize parameters.
- Deployment — deploy on server, set up monitoring and alerts.
Timelines and cost
Timelines: from 2 weeks for a simple algorithm to 2 months for a multi-factor system. Cost is calculated individually after strategy analysis and typically ranges from $3,000 to $20,000. A custom bot pays for itself in 3–6 months through reduced slippage and increased profitability.
Contact us to discuss your automated trading strategy. We'll create a custom trading bot tailored to your unique requirements.