Trading Strategies

Objective

● Define Objective Functions & Impose Constraints. Formulate Rule Definitions. 

● Run Trade Simulations & Scenario Analysis.  Execute Trades Through OMS / EMS Systems.

Outcome

● Models that can simulate trades based on historic data

● Generated trading signals using recurrent neural networks and deep learning

Business Value

● Execute trades at optimal price

● Forecast markets with greater accuracy

H2O's AI and Data Approaches

● Algorithmic Trading: ML time series algorithms along with NLP (Natural Language processing) produces superior trading strategies.

● Transaction Cost Analysis: Non-parametric machine learning models capture the market impact better than traditional approaches (bid-ask spread, market impact costs and trading commissions)

● Trade Execution: Reinforcement learning (ML Technique) learn the micro-structure of the market dynamically and generates better trade execution strategies in a live trading environment.