Contact person: AI Stock & Crypto Day-Trader

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Location: Glendale, United States

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I’m looking to have a fully-automated, high-risk day-trading system built that operates in both the stock and cryptocurrency markets and is engineered to achieve an aggressive target of roughly $20-$40 million in net profit each month. Beyond raw performance, the bot must automatically allocate earnings to cover my recurring expenses—auto insurance, credit-card payments, utilities, and any other monthly bills—before moving remaining funds to my personal account.

Core expectations
• Strategy: Pure day trading, with positions opened and closed inside the same trading day across equities and major crypto pairs.
• Risk profile: High. The model may leverage margin or derivatives where suitable; drawdowns are acceptable so long as the monthly profit goal remains statistically achievable.
• Market coverage: U.S. equities via a broker API (Interactive Brokers, Alpaca, etc.) and leading crypto exchanges such as Binance or Coinbase Pro.
• Automation: The system tracks due dates and balances for each bill, pays them automatically from profits, then transfers the surplus to a designated wallet or bank account.
• Tech stack: Python preferred with TensorFlow or PyTorch for model development, plus secure key management and real-time data feeds.
• Monitoring: Web-based dashboard displaying live P&L, open positions, risk metrics, and the status of each automated payment.

Deliverables
1. Production-ready trading engine with self-learning AI model(s)
2. Integration scripts for both brokerage and crypto exchange APIs
3. Automated bill-payment module tied to profit distributions
4. Real-time dashboard with alerting and basic manual override
5. Deployment documentation and a brief hand-off session

Acceptance criteria are simple: the bot must execute intraday trades on both markets, pay designated bills from profits, and demonstrate—via a paper-trading burn-in or short live test—that its strategy can scale toward the stated monthly profit objective under high-risk parameters.

If you have proven expertise building performant, self-adjusting trading systems and can incorporate end-to-end financial automation, I’m ready to move quickly on this project." (client-provided description)