Price: $18.99
(as of Dec 17, 2024 02:27:47 UTC – Details)

Table of contents

Introduction
Python Moving average crossover
Python Mean reversion strategy
Python breakout strategy
Python momentum trading Strategy
Python pairs trading
Complementary elements
Risk management
Robust backtesting
Advanced trading strategies
Intermediate trading strategies
Python trend following
Python Bollinger bands strategy
Python breakout pullback strategy
Python range trading
Python mean reversion with oscilators
Python Fibonacci Retracement strategy
Python mean reversion with volatility bands
Python moving averages Envelopes
Python mean reversion with RSI divergence
Python volume breakout strategies
AI/ML-based algo trading strategies
Python neural networks trading
Python reinforcement learning trading
Python genetic algorithm trading
Python sentiment analysis trading
Python ML ensemble trading strategy
Python Deep reinforcement Learning trading
Python Autoencoder based trading
Python Time series price forecasting
Python Deep generative Models for trading
Building an algorithmic trading system from scratch in Python can be an intricate process, but I can provide you with a general framework to get started. Please note that this is a high-level overview, and the actual implementation will depend on your specific trading strategy and requirements. Here are the key steps involved in building an algorithmic trading system:Define your trading strategy: Clearly define your trading strategy, including the entry and exit conditions, risk management rules, and any other relevant parameters. This step is crucial as it forms the foundation of your trading system.Obtain historical data: Collect historical market data for the financial instruments you intend to trade. You can use various data sources, such as financial data providers or APIs, to retrieve historical price data, volume, and other relevant information.Implement data preprocessing: Clean and preprocess the obtained data to ensure consistency and reliability. This step may involve handling missing values, adjusting for stock splits and dividends, applying technical indicators, and other data transformations.Develop trading signals: Based on your trading strategy, generate trading signals from the preprocessed data. These signals can be simple indicators (e.g., moving averages) or complex algorithms.Implement risk management: Define and implement risk management rules to control the size of your positions and manage potential losses. This may include setting stop-loss orders, position sizing algorithms, and portfolio diversification techniques.Backtesting: Test your trading strategy using historical data to evaluate its performance. Implement a backtesting framework that simulates trades based on your trading signals and calculates performance metrics such as profit and loss, Sharpe ratio, drawdowns, and others.Parameter optimization: Fine-tune your trading strategy by optimizing its parameters. You can use optimization techniques like grid search, genetic algorithms, or Bayesian optimization to find the optimal combination of parameters that maximize your chosen performance metric.Paper trading: Once you have a promising trading strategy, test it in a simulated or paper trading environment. This step allows you to evaluate its performance in real-time without risking actual capital. You can use historical or real-time data to execute trades and monitor the strategy’s performance.

ASIN ‏ : ‎ B0C5KNF2LX
Publisher ‏ : ‎ Independently published (May 17, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 154 pages
ISBN-13 ‏ : ‎ 979-8395009159
Item Weight ‏ : ‎ 10.2 ounces
Dimensions ‏ : ‎ 6 x 0.35 x 9 inches