Trading Bot Specification Trend-Based USD Profits

Tarea técnica

1. Objective

A fully automated trading bot that:
Trades USD-quoted assets (forex pairs like EUR/USD, GBP/USD, crypto markets with USD trading pairs, and USD-denominated stocks/ETFs).
Uses real-time data to detect trends and place entries/exits based on multiple strategies.
Maximizes profit while maintaining robust risk controls and drawdown limits.

2. High-Level Architecture

Components

Component Purpose
Data Ingestion Engine Collects real-time price data (ticks & candles)
Signal Generation Engine Applies multiple strategy algorithms
Risk & Money Management Module Size positions & control risk
Execution Engine Sends orders to broker/API
Portfolio Manager Tracks P/L, open positions, equity curve
Database/Logger Stores trade history, signals, performance logs
Monitoring UI / Alerts Real-time dashboard & notifications

 Supported APIs
Market Data: Binance, Coinbase Pro, OANDA, Alpaca, Interactive Brokers, etc.
Order Execution: Same API provider with trading permissions.
Websocket + REST combination for low latency.

3. *Data Input Requirements*

Market Data Type Interval
Forex OHLC + tick 1m, 5m, 15m, 1h
Crypto OHLC + tick 1m, 5m, 15m, 1h
Stocks/ETFs OHLC 1m, 5m, 15m

Data should include:
Bid/Ask prices
Volume
Time
Spread

4. *Strategy Framework*

Each strategy should generate a signal with:
ENTRY_SIGNAL
EXIT_SIGNAL
CONFIDENCE_SCORE (0–1)
RISK_SCORE (0–1)
Aggregated into a final decision

Strategies run in parallel — final trade decision is weighted consensus.

 5. *Core Trading Strategies*

*Trend Following (Primary)*
Based on EMA/SMA crossovers
Example: EMA(8) crosses above EMA(21) → LONG
EMA(8) crosses below EMA(21) → SHORT
Confirmation via MACD direction and slope
Longer trend filter: SMA(50) and SMA(200)

📌 Works well in trending markets

 *Momentum Breakouts*
Detect strong breakouts above resistance / below support
Filters:
RS (Relative Strength) over short timeframe
Volume spike filtering
Entry:
Price closes above resistance + volume > average

📌 Best for volatile markets

*Relative Strength Index (RSI) Strategy*
RSI 14 period
Buy when RSI < 30 and rising
Sell when RSI > 70 and falling
Optional exit:
RSI crosses back through 50

📌 Helps avoid overbought/oversold traps

*Bollinger Band Reversion*
Entry when price touches lower band and oversold
Exit when price reverts to mid-band or upper
Only valid if trend is neutral

📌 Works in sideways markets

*Breakout from Consolidation*
Detect consolidation via low ATR & narrow range
Place either buy or sell stop
Use ATR for dynamic stop loss

📌 Captures explosive moves

6. *Risk & Money Management*

Position Sizing
Fixed Fractional Risk: e.g., 1–2% of capital per trade
Maximum exposure across all positions: 10–15% of account balance

Stop Loss
ATR-based dynamic stops
Stop = Entry ± (1.5 × ATR)
Or chart-based support/resistance level

Take Profit
Risk:Reward target 1:2 minimum
Dynamic trailing stop

Daily Loss Limit
Stop trading for the day if drawdown > 3% of capital

Correlation Filtering
Avoid similar signals on strongly correlated pairs to reduce concentration risk

*7. Execution & Order Types*
Limit orders when possible
Market orders for fast-moving breakouts
OCO orders for combined stop & take profit
Slippage tolerance control

*8. Backtesting & Optimization*
Historical data backtest with walk-forward validation
Evaluate key metrics:
CAGR (Annualized Return)
Max Drawdown
Win Rate
Profit Factor
Sharpe Ratio

Optimization
Grid search on key parameters:
EMA lengths
RSI thresholds
ATR multipliers
Ensure out-of-sample stability

9. *Real-Time Monitoring & Alerts*

Alerts for:
Filled orders
Stop loss triggered
Take profit hit
Drawdown limit reached
System errors

Delivery:
SMS/Email/Telegram/Discord

10. *Tech Stack Options*

Languages
Python (Pandas, NumPy, TA-Lib)
Node.js (if low latency needed)
Optional C++/Rust for core execution layer (high throughput)

Databases
SQLite for local storage
TimescaleDB/PostgreSQL for cloud scale

Hosting
Cloud (AWS/GCP/Azure)
On-prem VPS

11. *Reporting & Logging*

Daily/Weekly/Monthly reports:
Equity curve graphs
Drawdown chart
Win/loss by strategy
Pair/Asset performance breakdown

Store:
Trade logs
Strategy signal logs
Performance snapshots

12. *Strategy Fusion Logic*

Final decision uses signal weighting model:

Score = Σ (Strategy_i_signal * strategy_weight_i * Confidence_i)

Only trigger trade if:
Score > Entry threshold (buy)
Score < Exit threshold (sell)

Weights can be dynamic and adjusted via performance.

*Bonus: AI/ML Enhancements*

Optional modules:
Reinforcement learning for adaptive entry timing
Clustering to detect regime shifts (Trend → Range)
Sentiment analysis feed for crypto/stock markets

*Example Execution Flow*
1. Fetch latest market data
2. Run all strategies → produce signals
3. Fuse signals → produce final trade decision
4. Compute position size & risk parameters
5. Execute orders via broker API
6. Monitor positions & update trailing stops
7. Log trades + send alerts.

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Información sobre el proyecto

Presupuesto
500+ USD

Cliente

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