Contents¶
Quick Start
Working with Data
User Guide
Api Reference
Examples
- Q22 All Platform Features: A Beginner’s Guide Strategy
- Full code
- 1) Load libraries
- 2) Data
- 3) Strategy. Weights allocation
- 4) Performance estimation
- 5) Submit Your strategy to the competition
- Strategy Guidelines
- Working with Data
- Loading Data
- Accessing Data Fields
- Working with xarray and pandas
- QNT Technical Indicators
- Frequently used functions
- Optimization
- How to find good parameters for my algorithm?
- Dynamic Assets Selection
- 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)
- How can you reduce slippage impace when trading?
- How to get the Sharpe ratio?
- How can you check the quality of your strategy?
- Common Reasons for Submission Rejection and Their Solutions
- 1) Missed call to write_output
- 2) Not eligible send to contest. In-Sample Sharpe must be larger than 1
- 3) Not enough bid information.
- Q22 Quick Start S&P500
- Technical Analysis using atr, lwma
- Technical Analysis using trix, ema
- Q18 Technical Analysis using Index Data
- 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
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