Specification
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1. Objective & Scope (High-Level Requirements)
Primary Objective
Build an automated trading bot that:
Identifies trading opportunities
Executes trades automatically or semi-automatically
Manages risk consistently
Logs and evaluates performance
Scope
Asset class: (e.g., Forex, Crypto, Stocks — adjustable)
Trading style: (Scalping / Day trading / Swing / Long-term)
Market access: API-based broker or exchange
Timeframe: (e.g., 1m, 5m, 15m, 1h)
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2. Functional Requirements
2.1 Market Data Handling
Fetch real-time and historical market data
Support:
OHLCV data
Multiple timeframes
Data validation and missing-data handling
2.2 Strategy Logic
Entry conditions
Exit conditions
Indicator calculation (e.g., MA, RSI, MACD, ATR)
Signal confirmation rules
Cool-down period between trades
2.3 Trade Execution
Place market / limit orders
Modify orders (SL / TP)
Cancel pending orders
Handle partial fills and slippage
2.4 Risk & Money Management
Max risk per trade (% of balance)
Stop-loss & take-profit logic
Max daily drawdown
Position sizing rules
Trade cap per day/session
2.5 State Management
Track:
Open positions
Account balance & equity
Trade history
Persist state across restarts
2.6 Logging & Reporting
Trade logs
Error logs
Performance metrics:
Win rate
Drawdown
Sharpe ratio
Export reports (CSV / JSON)
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3. Non-Functional Requirements
3.1 Performance
Low-latency execution
Efficient indicator computation
Rate-limit compliance
3.2 Reliability
Automatic reconnect on API failure
Graceful shutdown
Fail-safe on abnormal behavior
3.3 Security
Encrypted API keys
No hard-coded credentials
Read-only vs trading permissions separation
3.4 Maintainability
Modular architecture
Clear separation:
Data
Strategy
Execution
Risk
Config-driven parameters
---
4. System Architecture (Suggested)
[ Market Data Module ]
↓
[ Strategy Engine ]
↓
[ Risk Manager ]
↓
[ Execution Engine ]
↓
[ Broker / Exchange API ]
Supporting modules:
Logger
Backtesting Engine
Configuration Manager
---
5. Strategy Requirements (Trading Logic)
Define clearly:
Entry Rules
Indicator conditions
Trend filters
Volume confirmation
Time-based filters (sessions)
Exit Rules
Fixed SL/TP
Trailing stop
Indicator-based exit
Time-based exit
Risk Constraints
Risk per trade ≤ X%
Max open trades = N
Daily loss limit
---
6. Backtesting & Validation Requirements
Historical data coverage ≥ 1–3 years
Walk-forward testing
Out-of-sample testing
Monte Carlo simulations (optional)
Acceptance criteria:
Max drawdown ≤ threshold
Profit factor ≥ target
Consistent equity curve
---
7. Deployment Requirements
Environment:
Local / VPS / Cloud
OS compatibility
Continuous monitoring
Auto-restart capability
---
8. Compliance & Control (Optional but Important)
Manual override (kill switch)
Trading hours restrictions
Compliance with broker rules
Alert system (Telegram / Email)
---
9. Success Metrics (KPIs)
Net profit
Drawdown
Win rate
Risk-adjusted returns
Execution accuracy
---
10. Future Enhancements (Roadmap)
Machine learning strategies
Multi-asset support
Portfolio optimization
Adaptive risk models
---
Next Step (Important)
To customize this exactly for you, tell me:
1. Market (Forex / Crypto / Stocks)
2. Programming language (Python, JS, MQL, etc.)
3. Broker or exchange
4. Manual or fully automated
5. Strategy type (indicator-based, price action, ML)
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