Skip to content

Latest commit

 

History

History
190 lines (147 loc) · 8.02 KB

README.md

File metadata and controls

190 lines (147 loc) · 8.02 KB

Scalping Trading Bot - README

📌 Overview

This bot is an advanced scalping trading system, designed to execute trades based on technical indicators, volume confirmation, and fallback conditions. It operates on a score-based system, evaluating trade signals before executing buy/sell orders.

🚀 Features

  • Scalping Strategy: Executes trades based on a calculated entry score (base).
  • Trailing Profit & Stop-Loss: Supports dynamic trailing profit levels and stop-loss mechanisms.
  • Technical Indicators: Uses MACD, RSI, Bollinger Bands, VWAP, ATR, and Stochastic indicators.
  • Volume Confirmation: Ensures strong buy/sell signals.
  • Fallback Conditions: Allows trades when volume confirmation is weak.
  • Failover Support: Can pause trading based on external signals.
  • Portfolio Management: Retrieves available balance before executing orders.
  • Logging & Alerts: Detailed logs and push notifications for trade execution.

📦 Prerequisites

  • Python 3.x
  • Install required dependencies: 
    pip install pandas numpy requests tabulate logging

See requirements.txt for all the required libraries.

  • API access to Coinbase or other exchanges.
  • JSON configuration file for trade parameters.

🔧 Configuration

The bot relies on configuration parameters stored in JSON files:

  • trade_config.json: Defines trade parameters (BUY_THRESHOLD, SCALP_TARGET, etc.).
  • score_weights.json: Weights assigned to different indicators.
  • state.json: Stores the bot's state between runs.

Example trade_config.json:

{
  "BUY_THRESHOLD": 0.4,
  "SCALP_TARGET": 1.02,
  "STOP_LOSS": 0.98,
  "ENABLE_TRAILING_PROFIT": true,
  "ENABLE_DCA": true,
  "MAX_TRADES_PER_DAY": 5
}

🛠 Installation & Execution

  1. Clone the repository: 
    git clone https://github.com/msolomos/scalping-bots
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run the bot: 
    python trading_bot.py
  4. To reset the bot state: 
    python trading_bot.py --reset

📊 Technical Indicators Used

Indicator Purpose
MACD Measures trend momentum
RSI Detects overbought/oversold conditions
VWAP Price volume confirmation
Bollinger Bands Identifies volatility levels
ATR Measures volatility for dynamic thresholds
Stochastic Confirms momentum strength

🏦 DCA Strategy

  • If price drops below a threshold, it executes a second buy order to lower the average price.
  • If the price continues to fall, a third buy order is placed.
  • The break-even price is dynamically calculated.
  • Sells all positions when the price reaches the desired trailing profit or scalp target.

📡 Logging & Alerts

  • Logs all trade decisions and calculations.
  • Sends push notifications for important trade events.
  • Detailed logging of indicators, scores, and executed trades.

📌 Failover & Safety Mechanisms

  • Failover Mode: If an external bot suggests a "Pause", this bot will skip execution.
  • Stop-Loss Protection: Ensures losses are minimized.
  • Cooldown Management: Implements a cooldown after each sale to prevent immediate rebuying.

Trading Bot Execution Flow

Overview

This document outlines the operational flow and logic of the trading bot from the moment it is executed until it completes its trading cycle.

1. Initialization & Setup

  • The script starts by loading configuration settings, including trading parameters, API keys, and bot state.
  • It checks if there is a cooldown period active (to avoid consecutive trades).
  • The bot verifies whether it has received any urgent sell/buy signals from an external macro (Excel or another system).
  • If the bot is set to paused mode, it exits early without executing trades.
  • If failover bot integration is enabled, it checks whether the external decision allows trading.

2. Fetching Market Data

  • The bot retrieves live candlestick data from multiple sources (Coinbase, Binance, etc.).
  • It ensures that the data is structured correctly and converts timestamps into a DatetimeIndex.
  • If there is insufficient data, it will attempt alternative data sources.

3. Technical Indicator Calculation

  • The script computes various indicators, including:
    • Moving Averages (MA)
    • Moving Average Convergence Divergence (MACD)
    • Relative Strength Index (RSI)
    • Bollinger Bands
    • Volume Weighted Average Price (VWAP)
    • Average True Range (ATR)
    • Stochastic Oscillator & Stochastic RSI
    • Directional Movement Index (ADX)
  • The bot applies resampling logic if necessary to ensure smooth indicator calculations.

4. Scoring System & Buy Signal Evaluation

  • A weighted scoring system is applied to indicators, assigning a positive or negative score based on predefined thresholds.
  • If the overall score exceeds the BUY_THRESHOLD, the bot checks for volume confirmation.
  • If volume confirmation fails, the bot evaluates fallback conditions (ATR, Stochastic RSI).
  • If fallback conditions are met, the bot proceeds with a buy order.

5. Order Execution: Buying Process

  • Before placing an order, the bot:
    • Checks available portfolio balance.
    • Ensures that the required amount to buy is available.
    • Logs the trade reasoning (indicators & scores).
    • Sends an email notification via SendGrid.
  • If the buy order is successful:
    • The trade amount and entry price are recorded.
    • The highest price reached post-entry is tracked for trailing sell logic.

6. Trailing Profit & Stop-Loss Mechanism

  • The bot monitors price movement after entering a position.
  • If price exceeds a predefined scalp target, the trailing profit mechanism is activated.
  • If price falls below a dynamically calculated stop-loss, the bot forces an exit.
  • For positions that include DCA (Dollar Cost Averaging):
    • Second and third entries are triggered dynamically if the price drops beyond set thresholds.
    • A new break-even price is calculated.
    • If price reaches a trailing sell condition, all positions are exited.

7. Profit Calculation & Exit Strategy

  • The bot dynamically adjusts trailing profit thresholds based on market volatility (ATR-based adjustment).
  • When the trailing condition is met, a sell order is executed.
  • Profit/Loss, including fees, is logged, and push notifications are sent.
  • After exiting a trade, the bot enters cooldown mode to prevent immediate re-entry.

8. Bot Iteration & Loop Termination

  • If the daily profit target or maximum trade count is reached, the bot stops executing further trades.
  • The bot logs execution time and awaits the next scheduled run (via cronjob).

Summary

The bot follows a structured and automated decision-making process that:

  1. Analyzes the market through multiple technical indicators.
  2. Uses a weighted score system for trade validation.
  3. Implements DCA strategies for risk management.
  4. Uses trailing profits and stop-loss mechanisms for optimized exits.
  5. Ensures safe trading with volume confirmation and fallback logic.

The entire execution process is designed for efficiency, risk mitigation, and dynamic market adaptation.

Contributions

Contributions are welcome! This is my first attempt at building a project with Python, and I would greatly appreciate any help, feedback, or suggestions to improve the bot. Whether it's optimizing the code, adding new features, or simply pointing out areas of improvement, your input is invaluable. Feel free to submit issues or pull requests to make this project even better.

Disclaimer

This bot is provided for educational purposes only. Use it at your own risk. The creators are not responsible for any financial losses incurred.

📩 Contact & Support

For questions, issues, or contributions, reach out via GitHub Issues.

This bot is continuously evolving. 🚀 Stay tuned for updates!