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I will build automated spx trading bot, polymarket bot, schwab bot, etrade, moomoo bot
About this Gig
I will design, build, test, and deploy a production-ready automated trading bot focused on SPX (S&P 500 index) and SPX-related instruments (futures, ETFs, options). The service includes strategy development (momentum, mean-reversion, breakout, trend-following, hybrid), robust risk controls, historical backtesting, walk-forward validation, and optional integration for Polymarket, Charles Schwab, E*TRADE, and MooMoo.
What you'll get
- Customizable AI-enhanced algorithmic trading bot for SPX instruments (Python)
- Integration-ready modules or guidance for: Polymarket, Charles Schwab, E*TRADE, and MooMoo (subject to available APIs and account permissions)
- Strategy options: momentum, mean-reversion, volatility breakout, trend-following, hybrid
- Thorough backtesting and out-of-sample validation with performance metrics (Sharpe ratio, CAGR, max drawdown, win rate)
- Risk management: position sizing, stop-loss/take-profit, daily loss limits, max drawdown cutoff, kill-switch
- Paper trading simulator and live-execution adapter (requires API keys and proper broker permissions)
- Trade logging, performance reports (CSV/JSON), and optional lightweight dashboard/alerts
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FAQ
What instruments does the bot trade?
The bot is designed for SPX (S&P 500 index) and SPX futures (continuous contract or front-month). It can be adapted to related instruments on request.
Which brokers/platforms do you support?
I provide a MooMoo integration module (subject to available API access). The bot can also be adapted to other brokers/platforms that offer a documented API. Contact me with the broker name and API details.
Do you provide live trading or only paper trading?
Paper trading (simulated execution) is included by default. Live trading is optional and requires API keys and appropriate broker permissions. You must test in paper mode before switching to live.
What programming languages and libraries are used?
Primary language is Python. Typical libraries: pandas, numpy, scikit-learn / LightGBM / TensorFlow / PyTorch (optional), backtesting frameworks (backtrader, zipline, vectorbt) or a custom backtester. Execution wrappers use requests/websocket as needed.
How do you handle risk management?
The bot includes configurable risk controls such as per-trade stop-loss, take-profit, position sizing (fixed, proportional, Kelly if requested), daily loss limits, max drawdown cutoffs, and a manual/automatic kill-switch.
