edtechre/pybroker

Features

• Algorithmic Trading Frameworks - Provides a complete Python framework for developing, testing, and optimizing quantitative trading strategies.
• Trading Strategy Backtesters - Simulates trading rules against historical market data to generate comprehensive performance metrics and order logs.
• Backtesting Simulations - Provides an event-driven simulation engine that iterates through historical price bars to validate trading strategies.
• Confidence Interval Calculators - Estimates the reliability and confidence intervals of profit factors and risk ratios using bootstrap methods.
• Technical Indicator Calculators - Calculates technical trading metrics such as trend and direction using vectorized operations on price data.
• Crossover Signal Generators - Implements logic to identify crossovers between data series to generate boolean trading signals.
• Market Data Sources - Imports historical pricing and volume data from various external and internal sources.
• Portfolio Management - Tracks cash balances, equity, and open positions to manage a trading portfolio.
• Trading Portfolio Monitors - Tracks current holdings, available cash, and the history of filled or pending orders.
• Algorithmic Trading Strategies - Implements execution logic to buy or sell assets based on technical indicators and price thresholds.
• Position Tracking - Identifies all open positions and monitors the current status of specific symbols within the portfolio.
• Position Sizing Calculators - Calculates the optimal number of shares to trade based on custom risk-management logic.
• Market Data Acquisition - Provides systems for fetching historical and real-time price data for stocks and cryptocurrencies from external providers.
• Real-time Portfolio Tracking - Simulates a real-time account by tracking open positions, pending orders, and full trade history.
• Trading Strategy Robustness Validation - Implements quantitative validation of trading strategies using bootstrap sampling and walk-forward analysis.
• Trading Simulations - Provides a simulation environment incorporating portfolio management and slippage models to test trading rules.
• Performance Evaluation - Evaluates trading strategy success by calculating annualized performance metrics and statistical reliability.
• Trading Strategy Definitions - Provides a framework for defining entry and exit logic, including limit prices and position sizing.
• Financial Bar Data Retrieval - Retrieves historical OHLCV bar data for stocks and cryptocurrencies from multiple external providers.
• Financial Data Connectors - Provides an integration layer for fetching and caching historical stock and cryptocurrency data from external providers.
• Market Data Providers - Integrates a data layer for fetching, caching, and managing historical price data from external providers.
• Backtesting Workload Distribution - Distributes heavy backtesting and indicator workloads across multiple CPU cores to increase processing speed.
• Bootstrap Significance Testing - Provides statistically reliable performance metrics and confidence intervals for backtest results using random sampling bootstrapping.
• Bootstrapped Performance Validation - Applies random sampling to strategy results to generate confidence intervals and statistically reliable performance metrics.
• Indicator Computation Engines - Uses array-based operations and JIT compilation for high-performance calculation of technical indicators.
• Financial Performance Metrics - Converts trading returns and risk ratios into annualized figures based on defined trading periods.
• Bootstrapped Performance Statistics - Generates reliable performance statistics by applying randomized bootstrapping to strategy results.
• Statistical Significance Testing - Generates confidence intervals for key indicators to determine if strategy results are statistically significant.
• Algorithmic Trading - Provides a quantitative framework for calculating technical and volume-based indicators to automate trading strategies.
• Technical Analysis - Provides automated calculation of a wide range of mathematical technical indicators to identify market trends.
• Risk Management Simulations - Provides integrated portfolio risk management including position sizing and automated stop-loss triggers.
• Technical Indicator Engines - Provides a high-performance engine for defining and calculating technical indicators using vectorized operations and JIT compilation.
• Portfolio Metric Trackers - Monitors real-time indicators including realized profit and loss, market value, and margin usage.
• Model Instance Management - Enables retrieval of specific trained model instances by name and ticker to generate asset-specific signals.
• Portfolio Rebalancing - Adjusts asset weights through simulated trades to maintain target allocations or risk-return profiles.
• Trading Model Application - Supports loading external pre-trained models and configuring their preprocessing for use in strategies.
• Predictive Trading Models - Integrates pre-trained machine learning models and custom prediction functions to generate trading signals.
• Trading Signal ML Integrations - Allows the registration of custom training functions and indicators to generate ML-based predictions for asset returns.
• Model Feature Retrieval - Retrieves structured input data used for generating predictions for specific models and ticker symbols.
• Vectorized Technical Helpers - Ships built-in vectorized functions for common financial operations like highest-high, lowest-low, and crossovers.
• Execution Constraints - Defines critical trading constraints including starting cash, position limits, and fractional share support.
• Indicator Data Retrieval - Fetches registered indicator data for a ticker symbol in chronological order up to the current bar.
• Multi-Symbol Execution - Executes trades across multiple symbols simultaneously to support correlated actions like long-short pairs.
• Automated Exit Strategies - Automates the creation of take-profit and trailing stop orders to manage trade exits.
• Execution Constraints - Sets limits on the maximum number of concurrent positions and defines brokerage fee structures.
• Position Liquidations - Provides specialized methods to completely exit long or short positions within a trading strategy.
• Position Limits - Enforces hard constraints on the maximum number of concurrent open positions allowed.
• Stop-Loss Strategies - Implements automated stop-loss triggers to protect capital by closing positions at loss thresholds.
• Trading Direction Restrictions - Limits trading activity to long-only, short-only, or bidirectional modes during backtesting.
• Rotational Strategies - Simulates rotational trading by ranking assets based on indicators and rotating capital into top performers.
• Asset Ranking Algorithms - Prioritizes assets for purchase based on calculated scores to determine portfolio allocation.
• Margin - Calculates and enforces margin requirements specifically for short-selling positions.
• Data Caching - Saves downloaded market data and indicators locally to speed up iterative testing and development.
• Query Output Caches - Stores retrieved market data on disk by ticker and date to prevent repeated network requests.
• Intermediate Output Caching - Stores processed data and trained models locally to accelerate the development cycle and reduce redundant processing.
• Custom Data Source Integrations - Provides a base class for implementing proprietary or alternative data providers.
• Trading - Stores downloaded market data and trained models locally to reduce redundant computations during iteration.
• Disk Caching Systems - Implements disk-based caching for market data and computed indicators to avoid redundant network requests.
• Computational Result Caching - Saves computed indicator results to disk to avoid repeating expensive calculations across test runs.
• Market Data Aggregators - Aggregates combined columns and indicators to create structured input data required for machine learning models.
• Driver-Based Provider Architectures - Utilizes a driver-based architecture to allow custom market data providers to be plugged into the workflow.
• Cache Management - Provides mechanisms to remove stored information from caches to ensure the use of fresh market data.
• Financial Trade Cost Calculators - Calculates brokerage costs using constant per-order amounts or percentages of total trade value.
• Drawdown Metrics - Implements calculations to determine the likelihood that maximum drawdowns will remain within specific limits.
• Signal Information Analysis - Computes statistical properties like relative entropy and interquartile range to analyze the information content of trading signals.
• Order Execution Engines - Manages order execution parameters including limit prices and timing delays.
• Slippage Simulators - Simulates the difference between expected and actual executed prices to improve backtest accuracy.
• Percentage-Based Slippage Models - Simulates market slippage using customizable random percentage variations to increase simulation realism.
• Walkforward Analysis - Simulates real-world model behavior by testing strategy performance using sliding time windows.
• Configurable Fee Models - Simulates realistic trading costs by applying configurable brokerage fees and random price slippage.
• Multi-Core Parallelization - Distributes heavy indicator calculations and strategy evaluations across multiple CPU cores for improved performance.
• General Machine Learning - Library for algorithmic trading with machine learning.
• Trading and Backtesting - Algorithmic trading with machine learning integration.

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