and Trading Strategies Dr. Hilton Chan CEO Eniac FinTech Limited - PowerPoint PPT Presentation

FinTech Platform – Algorithmic Models and Trading Strategies Dr. Hilton Chan CEO Eniac FinTech Limited

Who are we? • Eniac is a FinTech company providing consultancy, design and development related to quantitative finance and algorithmic model building for financial institutes and private investors. • Our V-Algo FinTech platform provides a rendezvous for big data, algo developers, algo entrepreneurs and professional investors to enhance financial success, risk assessment and investment experience in the global financial markets.

Agenda 1. The Changing Financial Landscape (paradigm shift) 2. Algo Model Development and Innovation 3. FinTech Platform and Innovation 4. Enhancing financial success, risk assessment and investment experience

The changing financial landscape

Fintech – Quant Finance & Algo Models

Fintech – Quant Finance & Algo Models

Fintech – Quant Finance? Algo Trading?

Fintech – Quant Finance & Algo Trading

Growth in Algo Trading Source: Aite Group

Growth in Algo Trading Source: https://en.wikipedia.org/wiki/Algorithmic_trading

Gap Analysis Source: The IBM Financial Markets Framework

Algo Model Development

Example, Framing a research problem - “blackjack” 1. 2. Statistical model house/banker’s advantage (~0.5% - 3%) a) b) Law of large numbers 3. Order and Execution a) Data cleansing (random card generator) b) High frequency transactions/trading c) Risk controls - stand on 17 or more - minimal bet - table limit Algo model for the banker? player?

Algo development 1. Problem framing (opportunity identification) 2. Mathematical model a) Quantify the behaviours and factors (parameters) b) Accuracy vs. Complexity vs. Efficiency 3. Statistical model a) Probability 4. Descriptive vs Predictive models 5. Other scientific approaches 6. Computer logics and algorithms a) Data cleansing, mining, analytics, TS DBS b) Calculation c) Order & Execution d) Risk Controls

Simple Algo Models (Technical Analysis) Before data  data visualization  human + order & execution Now big data  data analytics/human  algo model/human  computer + order & execution

Big Data (interdisciplinary; innovative) Skirt length theory (Hemline theory) HKEx (data volume/day) Every day (day-data) – 1M bytes Every minute (minute-data) – 1G bytes Every tick (tick-data) – 60G bytes

More complicated Algo Models Pair Trading/Statistical Arbitrage - correlation - order & execution

Borrowing from other science disciplines? Signal Processing – Electrical & Computer Engineering

Borrowing from other science disciplines? Quantum Physics

Borrowing from other science disciplines? AI / Machine Learning – Computer Science

Algorithmic Models and Trading? Market volatility 1. Identify market opportunities, i.e. inefficiency, discrepancy, trends, pattern, etc 2. Observe and “predict/describe” the market - data modeling, data analytics, data mining - intelligence analysis (telecom, AML, weather forecast, etc.) 3. Risk controls (discipline) 4. Reduce human fallacies

V-Algo Critical Success Factors (Algo ICT Infrastructure / FinTech Innovation) Low Latency Big Data and Robust ICT Network Real-Time Time Series Risk SAFE Database Management

V-Algo A new entrepreneur experience for the young talents

Building the FinTech race track for algo testing

Q & A