Contents

Quick Start

• About the platform
• Building strategies
  • A basic example for futures
  • The detailed steps
  • Next steps
• Local development of trading strategies
  • Using a Conda environment
  • Pip environment
  • Google Colab support
  • Working example Jupyter Notebook or Jupyter Lab
  • How to check the qnt library

Working with Data

• Introduction
  • Working with Data
  • Frequently used functions
  • Xarray
  • Pandas
• Stocks
  • Stocks S&P500
  • Stocks NASDAQ100
  • Stocks NYSE and NASDAQ
• Indexes
  • Predicting NASDAQ 100 Stocks Using the SPX Index
• Cryptocurrency
  • Cryptocurrency Daily Data
  • Cryptocurrency Hourly Data
  • BTC Futures from spot price
• Futures
• U.S. Bureau of Labor Statistics (BLS Data)
• International Monetary Fund (IMF)
  • IMF Commodity Data
  • IMF Currency Data
• Fundamental Data
  • Example using return on equity (ROE)
  • Potential issues when working with fundamental data
  • Example: creating a custom financial indicator
  • Example using specific US-GAAP facts

User Guide

• Technical Indicators
  • QNT Technical Indicators
  • TA-Lib Technical Indicators
  • Examples
• Algorithm quality
  • Single-Pass Backtesting
  • Multi-Pass Backtesting
• Optimization
  • Single-pass strategy example
  • Optimizer function
• Competition filters
  • Technical filters
• Dynamic Assets Selection
  • Example strategy in Python
  • Applying to liquid assets
  • Trading stocks with different volatilities
  • Selecting stocks by Sharpe ratio
  • Volatility using a rolling window
  • Filtering stocks by normalized average true range (NATR)
• Risk Management
  • Exposure check
  • Applying exposure filters
  • Full code example

API reference

• Data loading
  • Inspecting the list of stocks
  • Loading stocks data
  • Inspecting the list of futures
  • Loading futures data
  • Loading Bitcoin futures data
  • Loading cryptocurrency daily data
  • Loading cryptocurrency hourly data
• Evaluation
  • Single-pass backtesting
  • Multi-pass backtesting
  • Stateful multi-pass backtesting
  • Statistics
• Operators
  • xarray
  • pandas
• Xarray
  • Overview
  • Indexing
  • Functions
• Your environment
• Translating code from Quantiacs Legacy
  • Technical changes
  • Example

Examples

• Q24 Crypto Guide Strategy
• Q22 Quick Start S&P500
• Q22 All Platform Features: A Beginner’s Guide
• Technical Analysis using trix, ema
• Q18 Technical Analysis using Index Data
• Technical Analysis using atr, lwma
• Machine Learning - LSTM - State
• Quick Start Fundamental Data
• Stateful Long-Short with Exits
• Q22 Stateful Machine Learning Neural Network
• Trend-Following Futures System
• Machine Learning with a Voting Classifier
• Trend-Following System with Custom Arguments
• Stateful Strategy Optimization
• Q20 Quick Start
• Q18 Quick Start
• Trading System Optimization
• Futures - BLS Macro Data
• Futures - IMF Currency Data
• Futures - IMF Commodity Data
• Trading System Optimization by Asset
• Predicting BTC Futures Using IMF Data

Theory

• What is algorithmic trading?
  • Вasic concepts
  • Long position vs. short position
  • Relative returns
  • Algorithm quality
  • In sample and out of sample
  • Improving the algorithm

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