FE8827 Quantitative Trading Strategies project - PowerPoint Presentation

Slide1

FE8827 Quantitative Trading Strategies projectHigh Frequency Trading Using Regime Switching Strategy

Huynh

Gia

Huy

(G1000176L)

Le Hoang Thai (G1000293G)Slide2

Reference“Developing High-Frequency Equities Trading Models”, Leandro Rafael Infantino, Savion

Itzhaki

, MBA thesis, MIT, June 2010.Slide3

ContentsSimply GuideModel Building: motivation, trading ideas, potential problems and implementation.Simulation Results: without/with transaction costs, development from the thesis.

Weaknesses of Trading Signals in the Thesis and Proposed Improvement: mean reverting signals and regime switching signals.Slide4

Simple GuideTo display performance statistics, run rsperf.m file: rsperf(index,

rf

,

lossthreshold

)

Input:

index

= 1: regime switching with transaction

costs.

index

= 2: regime switching without transaction costs

.

index

= 3: mean reverting with transaction costs.

index

= 4: mean reverting without transaction

costs.

rf

: risk free rate

lossthreshold

: omega loss

threshold

Ouput

: Omega ratio, Sharpe ratio, Omega Sharpe ratio,

MaxDD

(max drawdown) and

MaxDDD

(max drawdown duration).Slide5

Simple GuideTo start trading strategies, run regswitch.m file.

In this file, turn

on/off regime

switching by setting

regimeSwitching

variable to 1 to turn on and 0 to turn off.

regimeSwitching

= 1; % default

value

Change line 32 in this file to run simulation for a period of time. Default is whole year running from week 1 to week 52.

for

week=1:

52Slide6

Model building - MotivationIn high frequency environment, high precision of the stock return prediction is not required.Fundamental Law of Active Management

IR: Information Ratio

IC:

Information Coefficient (our skill)

Breadth: “the number of independent forecasts of exceptional return made per year”

Breadth very big => IC can be relatively small

=> prediction can be less preciseSlide7

Model building - Trading ideasUse Principle Component Analysis (PCA) as the basis to compute cumulative returnsDepending on the current strategy (mean-reversion or momentum), trading signals are generated if observed cumulative returns differ from model cumulative returns.

Predicted – observed > threshold => buy (sell)

Predicted – observed < -threshold => sell (buy)Slide8

Differences between this project and the paperNumber of stocks used in the simulation: only first 10 stocks (instead of 50) are chosen to represent the stock universe.Data source: the primary data used in this project is obtained from Thomson Reuters Tick History database, and from two major exchanges: NASDAQ and NYSE; whereas prices used in the thesis are from top of the book bid-ask quotes.

Model parameters: various model parameters are not specified in the thesis, thus simulation results can be affected by the choice of different parameter values.

Transaction costs are included.

Signal in the thesis is found insufficient and has been modified to improve stabilty and performance of returns.Slide9

Model building - Potential problems - data volumeHuge volume of data: more than 15Gbs of one year tick data to process (and that’s only for 10 stocks).Simulation time can be very long because of this.

Code optimization is important!Slide10

ImplementationMatlab is chosen as it provides many built-in mathematics functions suitable for rapid model development.Pre-process data before running simulation:Only one mid-price per second per stock

Need to duplicate values for missing seconds in raw data.

Processed data saved in separate .

csv

files.

Run simulation based on processed data.

All daily returns are saved in .

csv

files.Slide11

Some code optimization techniquesAlways pre-allocate memory for matrix and avoid changing matrix size constantly.Avoid loop as much as possible and make use of Vectorization

(performance increased dramatically!)

Use

Matlab

profiler to identify areas for improvement.

Normal for loop

Vectorization

i

= 0;

for t = 0:.01:10

i

=

i

+ 1;

y(

i

) = sin(t);

end

t = 0:.01:10;

y = sin(t);Slide12

Simulation results - thesisMean-reversion strategy, without transaction cost.Slide13

Simulation results – this projectMean-reversion strategy, without transaction cost.Slide14

Simulation results – this projectMean-reversion strategy, without transaction cost.Omega: 2.6741 (Loss threshold: 0)Sharpe: 0.3507

Omega Sharpe: 0.0022

Max Drawdown: 0.1566

Max Drawdown duration: 73 days.Slide15

Simulation results - thesisRegime switching strategy, without transaction cost.Slide16

Simulation results – this projectRegime switching strategy, without transaction cost.Slide17

Simulation results – this projectRegime switching strategy, without transaction cost.Omega: 0.7381 (Loss threshold: 0)

Sharpe

: -0.0903

Omega Sharpe

: -0.0014

Max Drawdown

: 0.5569

Max Drawdown duration

:

226 days.Slide18

CommentsThe different simulation results between this project and thesis can be due to:Different stock universeDifferent data source (therefore different mid-prices)

Parameters used (thresholds).

In the thesis, transaction cost is not taken into account and it is a important factor to consider in high frequency trading model.

All profits can be erased by transaction cost.

Next step for this project: include transaction cost!

To be realistic, transaction cost from Interactive Brokers is used; that is, $0.005 / share / tradeSlide19

Transaction costs: Modifications to existing strategyIn an attempt to factor in the transaction cost, the trading model is modified. Two potential places:Mark-up the threshold by the transaction cost, i.e.

new threshold = threshold + transaction cost

Lower the log returns. Use this new log returns as input for Principal Components Analysis

log_return

= log[(

current_price

- cost) /

previous_price

+ cost)]

This project uses the second approach.Slide20

Simulation result – modified modelMean-reversion strategy, with transaction cost.Slide21

Simulation results – this projectMean-reversion strategy, with transaction cost.Omega: 1.0131 (

Loss threshold: 0

)

Sharpe

:

0.0192

Omega Sharpe

:

0.0002315

Max Drawdown

: 0.2373

Max Drawdown duration: 68 days.Slide22

Simulation result – modified modelModified regime switching strategy, with transaction cost.Slide23

Simulation results – this projectMean-reversion strategy, with transaction cost.Omega: 0.7805 (

Loss threshold: 0

)

Sharpe

: -0.0708

Omega Sharpe

: -0.0010

Max

Drawdown

: 0.4941

Max Drawdown duration

: 248 days.Slide24

Weaknesses of the trading signals in the thesis – Proposed ImprovementsMean reverting signal: In the thesis, if mean reverting signal > 0, we buy and sell when signal < 0. It includes noise due to rounding or computational issues.

Solution: set a threshold to filter away noise that creates fault trades.

This threshold after trial and error has been determined to be 0.0001 (parameter 1)Slide25

Weaknesses of the trading signals in the thesis - Proposed ImprovementsRegime Switching Signals:The authors use difference in two consecutive Euclidean distance to signal the regime switching:If E

H

(t) - E

H

(t-1) > 0: momentum regime

If E

H

(t) - E

H

(t-1) < = 0: mean reverting regime

This signals introduce noise and cause fault signals and consequently fault trades.Slide26

Weaknesses of the trading signals in the thesis - Proposed ImprovementsRegime Switching Signals – Noise

Diagram of Euclidean distance difference (

E

H

(t) - E

H

(t-1

)) is shown above.

According to the authors, the strategy keeps changing the regimes as signals swing around 0 from positive to negative. Slide27

Weaknesses of the trading signals in the thesis - Proposed ImprovementsRegime Switching Signals – Fault Signals

Diagram of Euclidean distance difference (

E

H

(t) - E

H

(t-1

)) is shown above and

Euclidean

distance

(

E

H(t)) below.One strong pulse in E distance creates one regime switching signal but it contains one up pulse and one down pulse in E distance difference. According to the authors,

it creates two signals that causes the system to switch back and forth (mean reverting -> momentum -> mean reverting) within <150 seconds. Slide28

Weaknesses of the trading signals in the thesis - Proposed ImprovementsRegime Switching Signals – Noise – Solution

We set a threshold of 2 standard deviation to filter out noise (parameter 2). Magnitude falls between ± 2

std

dev

is considered insignificant.

Instead of using differences of 2 consecutive E distance (

E

H

(t) - E

H

(t-1)), we use EH

(t) – ewma5(EH(t)) where ewma5(EH(t))

is equally weighted moving averages value of previous 5 seconds of E distance (parameter 3).Ignore 2 consecutive regime switching signals fall into a timespan of less than 250 seconds to remove fault signals (parameter 4).Slide29

ConclusionsSimulation returns are very sensitive to parameters used.All 4 parameters can be improved by employing optimization.Due to the time constraint, we leave this part for future development.