20161110 quantstrat in seattle

Today’s Slideshttps://goo.gl/xkH9QY

go to the 5th page and click out all links on the page

And reserved your port on the spreadsheet

Mining Trading Strategies with R

using quantstrat and R packages

George (Chia-Chi) Chang20161110

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司. All rights reserved.

Unless otherwise indicated, all materials on these pages are copyrighted by 木刻思股份有限公司. All rights reserved. No part of these pages, either text or image may be used for any purpose other than personal use. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission.

Quick Surveys● How many of you use R ? ● How many of you did have some REAL trading experience in financial market

? ● What kind of signals & strategies did you use in trading ? (your intuition is also

one kind of useful signal too)

● Did anyone use MultiChart ? AmiBroker ? Interactive Broker APIs ?● Did anyone use quantmod ? blotter ? quantstrat ?

Learning By HackingLAB / Collaborative Notes / Broadcasting Notes

Sample Codeshttps://github.com/c3h3/quantstrat-seattle-20161110

● Quick Surveys● Architecture of Trading System● [Hands-on] quantmod 101● History of Backtesting with R

○ [Hands-on] PerformanceAnalytics 101○ [Hands-on] blotter 101○ [Hands-on] quantstrat 101

● Basic Concepts of Quantitative Trading● [Hands-on] quantstrat 102● [Hands-on] quantmod with ML● Blindness of ML● Two kind of Backtesting● Self Introduction & TW.R & MLDM Monday

Outlines

Architecture of Trading SystemData -> Signals & Strategy ->

Transaction & Actions -> Rewards & Results

Architecture of Trading System● Data

○ Real-time Data○ Historical Data

● Signals & Strategy○ Rule-Based○ Model-Based○ Human-Based (Intuition-Based)

● Transaction & Actions○ Enter & Exit ○ Long & Short○ Position Sizing

● Rewards & Results○ Win | Lose○ Metrics (winning prob, WLR, DrawDown, … )

Real-Time Trading Backtesting & Mining

Real-Time Data

Signals & Strategy

Transactions & Actions

Rewards & Results

Historical Data

Mining Strategies & Objectives

(human brain)

Indicators & Signals & Action Rules(Bottom-Up Versus Top-Down)

Strategy Metrics & Backtesting Results

Parameter Search & Optimization

Final Results & Strategy

History of Backtesting with R

PerformanceAnalytics (2007)blotter (2008)

quantstrat (2010)

quantmod 101Hands-on LAB

quantmod 101: getSymbols & ChartSeries

Yahoo! Finance

Yahoo! Finance

PerformanceAnalytics101

Hands-on LAB

PerformanceAnalytics:● Data Retrieving & Technical Indicator

○ quantmod:: getSymbol○ quantmod:: ChartSeries○ TTR:: SMA

● Performance analysis○ PerformanceAnalytics:: CalculateReturns○ PerformanceAnalytics::charts.PerformanceSummary○ PerformanceAnalytics::chart.RollingPerformance○ chart.RelativePerformance○ PerformanceAnalytics::chart.Drawdown○ PerformanceAnalytics::chart.RiskReturnScatter○ PerformanceAnalytics::SharpeRatio○ PerformanceAnalytics::VaR

blottor 101Hands-on LAB

Blotter Important Functions: ● initializtion:

○ blotter::initPort○ blotter::initAcct

● simulation:○ blotter::addTxn○ blotter::updatePortf○ blotter::updateAcct○ blotter::updateEndEq○ blotter::getPosQty

● plot & summary:○ blotter::chart.Posn○ blotter::chart.ME○ blotter::PortfReturns○ blotter::getAccount○ blotter::getPortflio○ blotter::getTxns○ blotter::tradeStats

quantstrat 101Hands-on LAB

Quantstrat: ● init strategy object:

○ quantstrat::strategy

● define strategies:○ quantstrat::add.indicator○ quantstrat::add.signal○ quantstrat::add.rule

● Execution in Backtesting Process:○ quantstrat::sigCrossover○ quantstrat::sigComparison○ quantstrat::ruleSignal○ quantstrat::addOrder

● get Strategy & Order Book○ quantstrat::getStrategy○ quantstrat::getOrderBook

Architecture of quantstratcreated by

C.Y. Yen

who is the Founder of RLadies.TW

Basic Concepts of making quantitative trading strategy

What is the only wayto make money from market ?

The Only Way is ... ● Buy Low and Sell High● Trends up: Buy first, then sell● Trends down: Sell first, then buy

Arbitrage or Anti-arbitrageE = pW - (1-p)L - T > 0

Arbitrage or Anti-arbitrageAssum T = 0, WLR = W/L

p > 1 / (1+WLR)

Signals in Trading System● Entry Signals

○ Primary Signals○ Filters

● Exit Signals○ Stop Signals○ Limit Signals○ Time-out Signals○ Filters

Stop Price

Limit Price

Exit Signal

Timeout Signal

Entry Signal & Price

Signal Filters

quantstrat 102create indicators by yourself

create signals by yourself

quantmod with MLClassification

Principle Component AnalysisClustering

Blindness of MLBlindness from PCA

Blindness from Vector Quantization

The two keysHelp you apply machine learning

in the real world

Can Learn ONLYThrough Real

Practice

Can Learn fromSchool or Practice

Modeling Procedures:● Choose a Real Problem● Collecting Related Data● Choose a method convert Data to Vectors (or Tensors)● Decompose Real Problem into several ML or Math Problems● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

(ref: Moving Forward through the Darkness)

Machine could NOT Learn by itself.

It just like a child.It learn by training data !

sometimes would learn badly!

When orange-apple classifier meet an banana?

Two Kind of BacktestingTop-Down & Button-Up

Entry Signal & Price

Timeout SignalStop Price

Exit Signal

After Event:How to evaluate event?

Before Event:How to predict event?

Limit Price

Entry Signal & Price

Timeout SignalStop Price

Exit Signal

Before Event:How to predict event?

Limit Price

X Y

After Event:How to evaluate event?

Button-Up: P(e(Y)|X)

Entry Signal & Price

Timeout SignalStop Price

Exit Signal

Before Event:How to predict event?

Limit Price

X Y

After Event:How to evaluate event?

Top-Down: P(X|e(Y))

Entry Signal & Price

Timeout SignalStop Price

Exit Signal

Before Event:How to predict event?

Limit Price

X Y

After Event:How to evaluate event?

ML & DL: Y = f(X)

Button-UpProbabilistic Modeling

Exit Signal

After Event:How to evaluate event?

Before Event:How to predict event?

Limit Price

Timeout Signal

Entry Signal & Price

Stop Price

Limit = 0.06 / Stop = 0.02

Limit = 0.06 / Stop = 0.06

Self Introductionand TW.R & MLDM Monday

About Chia-Chi (George) ● Organizer of Taiwan R User Group and MLDM Monday● 7 years experience in quantitative trading in future & option market● 5 years consultant experience in machine learning & data mining● 4 years experience in e-commerce (consultant & join SaaS teams)● 4 years experience in building of recommendation and search engine ● Volunteer in PyCon APAC 2014 (program officer)● Volunteer in PyCon APAC 2015 (program officer)

4 Years agoI read a Book ……

I started a community

Welcome to MLDM Monday

when you visit Taiwan next time !

Welcome to join us !

Thank youfor your attention !