In modern limit order book(LOB)-driven financial markets, we contribute algorithmic trading strategies for multi-asset classes, including traditional assets, equities, futures contracts, and emerging assets like cryptocurrencies. With the capabilities of a computer system, algorithmic trading can be applied to short-term, even minute intervals.

The first contribution is algorithmic trading strategies for Hong Kong stocks. We generate trading signals at the minute intervals, implement SAR & MACD modules and back-test 391 stocks.

The second contribution is statistical arbitrage across different futures exchanges. We explore precious metal futures contracts listed on Shanghai Futures Exchange, Chicago Mercantile Exchange, and Japan Exchange Group. Our results show the correlation observa...[ Read more ]

In modern limit order book(LOB)-driven financial markets, we contribute algorithmic trading strategies for multi-asset classes, including traditional assets, equities, futures contracts, and emerging assets like cryptocurrencies. With the capabilities of a computer system, algorithmic trading can be applied to short-term, even minute intervals.

The first contribution is algorithmic trading strategies for Hong Kong stocks. We generate trading signals at the minute intervals, implement SAR & MACD modules and back-test 391 stocks.

The second contribution is statistical arbitrage across different futures exchanges. We explore precious metal futures contracts listed on Shanghai Futures Exchange, Chicago Mercantile Exchange, and Japan Exchange Group. Our results show the correlation observations for futures contracts in 2020.

Apart from traditional asset classes, the third contribution is cryptocurrency spots and perpetual futures trading with technical indicators including multi-strategies, e.g., shorting. We backtest 2019-2021 multiple periods.

To understand the underlying distribution of each asset, we create a simulator to generate orders based on Wasserstein GAN as the fourth contribution. We compare the original data with the generated real and fake data using Kolmogorov-Smirnov tests, MSE, MAE to quantify our model performance.

Multi-asset classes investment boosts overall portfolio performance with risks diversification across multiple classes. Here we compare three assets: equities, futures, and cryptocurrency from volatility and liquidity. The combination of adopting minute intervals observations with a systematic computation across equities, commodity futures, and emerging cryptos can be an exciting topic. This thesis lays out a structure for algorithmic trading strategies and simulations.