H Trading

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



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4. System Architecture (Suggested)

[ Market Data Module ]
          ↓
[ Strategy Engine ]
          ↓
[ Risk Manager ]
          ↓
[ Execution Engine ]
          ↓
[ Broker / Exchange API ]

Supporting modules:

Logger

Backtesting Engine

Configuration Manager



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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



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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



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7. Deployment Requirements

Environment:

Local / VPS / Cloud


OS compatibility

Continuous monitoring

Auto-restart capability



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8. Compliance & Control (Optional but Important)

Manual override (kill switch)

Trading hours restrictions

Compliance with broker rules

Alert system (Telegram / Email)



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9. Success Metrics (KPIs)

Net profit

Drawdown

Win rate

Risk-adjusted returns

Execution accuracy



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10. Future Enhancements (Roadmap)

Machine learning strategies

Multi-asset support

Portfolio optimization

Adaptive risk models



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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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Project information

Budget
30 USD
Deadline
to 30 day(s)