"Applying Deep Learning Techniques to Financial Time Series" by Scott Treloar, Founder of Noviscient

Applying Deep Learning to Financial Time Series

QuantCon Singapore

November 2016

Introduction

Founder of Noviscient – a technology-led, research and proprietary trading firm.

We apply statistical and machine learning technologies to investment management problems.

• Experience: 20 years in finance

• Learning: BEng, MBA, MQF, PhD candidate

• Programming: Python (Matlab, C)

• Trading: Asian markets

• [email protected]

November 2016 Noviscient Pte Ltd (www.noviscient.com) 2

mailto:[email protected]

Introduction

In this presentation we discuss a framework for predictive trading using deep learning

• The context

• General approach to systematic trading

• The challenge (prediction)

• Machine learning and deep learning

• An approach

• Some examples

• Wrap up


The context: problems in traditional asset management


Context: drivers of change


The North American asset management industry is on the brink of a once-in-a-generation shift in competitive dynamics, due to five converging trends

McKinsey: Thriving in the New Abnormal (November 2016)

• The end of 30 years of exceptional investment returns

• A shake-up in active management - a large pool of benchmark-hugging active assets (up to $8 trillion) will be up for grabs over the next several years

• The decline in average returns will also spur a third significant trend, a boost in the steady stream of assets moving into alternative investments

• A true digital revolution in data and analytics – firms that can deliver both investment and operational alpha will be industry leaders in the coming years

• An era of heightened regulation increasing legal and compliance costs and raising barriers to entry

Context: the future of asset management


Change is coming


General approach to systematic trading

Necessary conditions for a successful systematic trading business:

1. Source of trading ideas

2. Strategy development framework to review the ideas and find the edge

3. Infrastructure to execute on the edge


Necessary Condition 1 – source of trading ideas


Necessary Condition 2 – strategy development framework

Trading is an information game. We are trying to input various traditional and alternative data sources into one or more models and combine the output to produce an attractive portfolio return distribution.

• Choice of market instruments and trading frequency• Development of trading rules for entry and exit and any trailing stops or profit takes• Position-sizing and target volatility calculation• Method for incorporation into the broader portfolio• Robust backtesting methodology• Approach to forward testing

All within a hypothesis-testing framework to reduce over-fitting problems


Necessary Condition 3 – infrastructure


Principles

• Cloud-hosted

• Open-source

• Single language

• Modular

• APIs

• Messaging

Digression


Why are we here at this conference?

• I think it is because we don’t really want to be employees in large institutions

• Maybe we want to be our own boss, or work in small entrepreneurial teams where we control our own fate?

• The infrastructure for institutional-level, systematic trading is becoming available through groups like Quantopian and Noviscient

• We are getting to the point where we can build a career in systematic trading

Digression

Why now for systematic trading?

Quantitative trading segments:

• Low frequency risk premia traders

• Mid frequency systematic traders

• High frequency market makers

The scarce resource for systematic trading is not capital or infrastructure.

It is alpha generating capability.


Capital

(risk premia)

Ideas

(alpha)

Infrastructure

(speed)

Strategy development - the challenge

Objective

• Prediction – we want to predict the future given only the information we have today

Problems

• Inputs are unknown

• Model linking the inputs to the outputs is unknown

• Noise / signal ratio is very high

• The data generating process generating the outputs is likely to be time-varying


Input (t) Output (t+1)Model (t)

Enter machine learning


Why now?


Machine Learning

Algorithms

Cloud Computing

Big Data

Current applications


• http://www.deeplearningbook.org/

Often quite static and benefit from big data.

Strategy development framework

Systematic trading is a very competitive space with relatively low barriers to entry.

To build a sustainable business you need a robust and differentiated approach to identify alpha opportunities.

This means being set up to take advantage of


Old New

Simple data Complex data

Linear models Non-linear models

Batch processing Online processing

Static Dynamic

Dynamic Adaptive

Daemon

Why?

• Complex data – sensors, data feeds, voice …

• Non-linear processing – filtering

• Online

• Dynamic

• Adaptive


Some of our research areas

Recursive least square filters

• an online filter that recursively looks for coefficients that minimize a weighted linear least squares cost function



Extreme learning machines

• Feedforward neural network for classification (up / down) with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes

• Very fast – only (?) matrix inversion

• Can use with recursive least squares



Use of autoencoding and deep learning

• Learn a representation of the data – similar to dimensionality reduction

• Calculate the mutual information between the stock an its encoded / decoded representation

• Use deep learning on a ‘smart’ subset of the stocks to achieve a certain portfolio return objective


Wrap up

• The future is systematic

• Still early days – we are on or near the ground floor for finance applications

• We also need to go towards online, non-linear, dynamic and adaptive

• If you want to learn more from us at Noviscient we have the first of series of workshops being held in Singapore on 5-6 December (see our website for details).

Questions?


Date post:	21-Jan-2018
Category:	Economy & Finance
Upload:	quantopian
View:	3,087 times
Download:	0 times

"Applying Deep Learning Techniques to Financial Time Series" by Scott Treloar, Founder of Noviscient

Economy & Finance