PraAnj / SpatialLOB-Learning-spatial-properties-of-Limit-Order-Book
SpatialLOB is designed for stock price movement prediction by exploiting spatial and temporal properties of the Limit Order books. SpatialLOB consists of a deep network combining CNN and Stacked GRU.
☆9Updated 3 years ago
Alternatives and similar repositories for SpatialLOB-Learning-spatial-properties-of-Limit-Order-Book:
Users that are interested in SpatialLOB-Learning-spatial-properties-of-Limit-Order-Book are comparing it to the libraries listed below
- Limit Order Book for high-frequency trading (HFT) strategies using data science approaches☆21Updated 3 years ago
- We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cos…☆10Updated 4 years ago
- Paper: https://arxiv.org/pdf/2008.12275.pdf☆26Updated 4 years ago
- ☆10Updated last year
- Deep Q-Learning Auto Market Maker☆12Updated 3 years ago
- Apply Box&Tiao to generate stationary price spread series in steel industry commodity futures market for pair trading☆12Updated 2 years ago
- The intraday seasonality of volatility and trading volume in the cryptocurrency market☆15Updated this week
- Exploring Optimal Order Execution in Simulated Limit Order Books☆16Updated 2 years ago
- A Deep Reinforcement Learning model for high volume and frequency Forex Portfolio Management☆11Updated 2 years ago
- High frequency trading algorithm for Bitmex☆21Updated 4 years ago
- Limit Order Book Convolutional Neural Network trading bot☆14Updated 2 years ago
- Building a High Frequency Trading Engine with Neural Networks☆12Updated 6 years ago
- This is for the capstone project "Optimal Execution of a VWAP order".☆31Updated 5 years ago
- Time-Series Momentum Strategies☆10Updated 6 years ago
- 🏦 Collect quant trade strategy and deep learning trading implementation☆12Updated 6 years ago
- Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market…☆15Updated 3 years ago
- Vpin caculation and backtesting☆13Updated 5 years ago
- Implementation for "Statistical arbitrage in the US equities market" by Marco Avellaneda and Jeong-hyun Lee☆18Updated 6 years ago
- This project analysed financial data and designed trading strategies by machine learning models.☆10Updated 6 years ago
- Predicting a Stock Price Using a Genetic Algorithm☆16Updated 6 years ago
- Use total, upper, down, relative volatility factors to find Alpha. Implement whole trading process & back-test with visualization.☆12Updated 3 years ago
- Exercises in 'Advances in Financial Machine Learning' by Lopez de Prado☆3Updated last year
- ☆20Updated 4 years ago
- Machine learning trading method using meta-labeling. You can see the details in 'Advances in Financial Machine Learning' by Lopez de Prad…☆13Updated 3 years ago
- I built a real-time streaming data pipeline using kafka, consuming deribit-api-v2 limit order book prices 📈 and transforming them into …☆20Updated 2 years ago
- High Frequency Trading Strategies☆41Updated 7 years ago
- Backtesting a simple Buy Low Sell High Strategy☆9Updated 3 years ago
- Limit Orderbook CNN model implementation for ETH-BTC (buy-low-sell-high indicator)☆17Updated last year
- Substantial backtesting of statistical arbitrage pairs trading with crypto-currencies☆22Updated 4 years ago
- Create a mid-price classifier for limit order books using a CNN and LSTM☆14Updated 4 years ago