Self sufficient Trading robot MODIFICATIONS v1

Great! Let's outline Version 1 (v1) modifications for a Self-Sufficient Trading Robot. Below is a structured template for what v1 could include. If you're already working with code or specific platforms (like MetaTrader, TradingView, Python, etc.), I can tailor this accordingly.

🔧 Self-Sufficient Trading Robot – Modifications v1

✅ Objective

Implement core features for a fully autonomous trading robot that:

• Analyzes market data

• Makes trade decisions

• Executes trades

• Manages risk

• Learns or adapts over time (basic level)

1. Core Functionality

• Market Data Feed Integration

  • Real-time price data via API (e.g., Binance, Alpaca, MetaTrader)

  • Candlestick or tick-level data handling

• Strategy Logic

  • Technical indicator(s): e.g., RSI, MACD, EMA crossover

  • Entry/exit signals

• Trade Execution

  • API-based execution

  • Market or limit order support

  • Position sizing logic

2. Risk Management

• Stop-loss / Take-profit system

• Max daily loss or trade count limits

• Position size based on account balance (e.g., % risk per trade)

• Leverage control (if applicable)

3. Monitoring & Logging

• Trade journal (log all actions)

• Error handling and exception logs

• Daily PnL summary (email/Telegram optional)

4. Basic Autonomy Features

• Auto-restart on error or disconnection

• Session handling (detects market open/close)

• Health checks (pings API and halts trading if unstable)

5. Adaptation / Learning (Lightweight)

• Adjusts indicators’ sensitivity over time based on win/loss ratio

• Simple moving average tuning (backtest weekly results)

• Trade filtering based on past performance (e.g., avoid Mondays if historically bad)

6. Security & Fail-Safes

• API key encryption

• Max drawdown shutdown trigger

• Cloud sync or remote kill-switch

7. Optional: UI / Dashboard (Optional for v1)

• CLI/terminal dashboard with:

  • Current positions

  • Upcoming signals

  • Portfolio performance summary

🗂 Recommended Technologies

• Language: Python (or MQL4/5 for MetaTrader, Pine Script for TradingView)

• Broker/API: Alpaca, Interactive Brokers, Binance, MetaTrader

• Libraries:

  • ccxt , pandas , ta-lib , backtrader , matplotlib

  • schedule , telepot (if using Telegram alerts)

• Storage: SQLite or local file logs (upgrade to PostgreSQL for v2)

🔄 Next Steps

1. Define the trading strategy logic

2. Select the API/platform

3. Implement base architecture

4. Backtest and forward test on demo account

5. Monitor performance for minimum 2 weeks

Would you like a starter codebase or a specific modification implemented in your current setup? If you're already working in a platform (e.g., MetaTrader or Binance API in Python), I can start modifying directly from there.