Algo Traders Conference - Mumbai

5th May, 2018

www.algoji.com

Coding Cases

Saurabh Lohiya Founder, AlgoJi

Example: Amibroker ATS Charting/Strategy Software

• Real-Time Market Data

Amibroker Trading Logic/ Alpha Strategy

• BackTesting Databse

AFL

Execution Strategy

• Risk Management

In-house CTCL/ NEST/NOW/ODIN

Example: Amibroker ATS

Example: Python ATS

Example: Amibroker ATS

Amibroker

Database

FoxTrader

AFL

Python

Strategy

In-house CTCL/ NEST/NOW/ODIN

In-house CTCL/ NEST/NOW/ODIN

In-house CTCL/ NEST/NOW/ODIN

Our Random World (depicted via PnL charts)

Mobile Algo Trading App

Python | Amibroker | Manual 1

3

Charting Application + Analytics Engine

Broker Admin Panel

2

Secure Mobile App Backend

5 Client Mobile App

4

Transaction Facilitator (Broker end) Copyright AlgoJi Enterprises, all rights reserved

Thank You [email protected]

www.algoji.com

Data Science and Trading Dr. Hari, PhD. IISc Bangalore Nitish Mukherjee, Director, IIQF Date : 5th May 2018

Overview and Content • Overview on python • Installing Python • Data Science Application to Algo Trading

Algo Trading • Algo trading in India and US • US market replication to Indian market • Last few years it has picked momentum in Indian market

Magic of Compounding

Compounding • As Albert Einstein said, 'compounding is something one who understands earns it and one who doesn't understand pays it'. • Compounding works best with equity asset. • Reason why world's richest men list include people who have created wealth by taking advantage of compounding with their equity investment.

Quantitative Research and Data Science • Compounded annual growth on different percent • 7% growth through fixed deposit or • 10% growth though mutual fund, SIP • 15% growth though quantitative investment in equity • 30% growth though quantitative investment and trading in equity • Risk and Reward management (Data Science and Machine learning)

Learning and courses: • Algo Trading • Common studies and Terminology • Process of strategy Design

• Python • Programming concepts

• Machine Learning, Data Science in trading

Scientific Trading • Trading Premise: Data, observations, Ideas about instruments and markets that can make money • Creating strategy: Trading premise are used to create relationship in the form of Buy-Sell rules. A complete set of trade entry and trade exist rules make a strategy • Back testing strategy: Back testing is the process of testing a strategy on historical data set to estimate its profitability in live trading • Live Execution: Sending orders to the broker/exchange, managing orders and their latency is a daunting task

Miniconda • Conda is a package management and environment management system • Miniconda is smaller in size compare to Anaconda. • We still need to install all the packages which are not included in Anaconda

PyCharm • PyCharm is an integrated Development Environment useful to manage code and data files. • It makes building trading models very easy.

Python Libraries • Numpy: Numerical computations • Scipy: Scientific computation • Sci-kit Learn: Machine learning models • Pandas: panel data • Matplotlib: plotting tools

Conda • • • • •

$conda install numpy $conda create -n myenv python Conda version (the number of the version installed) conda --version Update conda (Conda compares versions and then displays what is available to install) • If a newer version of conda is available, type y to update • conda update conda • List of environments (The active environment is the one with an asterisk (*))

Conda • conda info --envs • Activate Environment • activate myenv • Deactivate Environment ( When the environment is deactivated, its name is no longer shown in your prompt, and the asterisk (*) returns to base) • deactivate

Jupyter notebook • Notebook provide Interactive Platform • Server-client application that allows editing and running notebook documents via a web browser • Jupyter notebook app can be executed on a local desktop requiring no internet access.

Data Science Application to Algo Trading • Statistical Arbitrage • Linear regression • Time Series Models • CVAR (Cointegrated vector auto regression)

• Limit order submission • Probabilistic models • Hidden Markov Models

• Classification and Regression models

www.algoji.com

Technical Analysis Dr. Hari, PhD. IISc Bangalore Nitish Mukherjee, Director, IIQF Date : 5th May 2018

Overview and content • ROC • MACD • RSI • Machine Learning

ROC • The Rate-of-Change (ROC) indicator is a pure momentum oscillator that measures the percent change in price from one period to the next. • The ROC calculation compares the current price with the price ‘n’ periods ago. • ROC signals include centerline crossovers, divergences and overbought-oversold reading.

ROC Calculation • ROC = [(Close - Close n periods ago) / (Close n periods ago)] * 100 • In general, prices are rising as long as the Rate-of-Change remains positive. Conversely, prices are falling when the Rate-of-Change is negative.

Moving Average Crossover • extremely well-known simplistic momentum strategy. • Create two separate simple moving average filters, with varying lookback periods • Signals to purchase the asset when the shorter lookback moving average exceeds the longer lookback moving average

MACD • Moving Average Convergence Divergence: A Market timing Indicator • Short term moving window • Long term moving window • Short term exponential moving average • Long term exponential moving average

MACD • MACD = SMA – LMA • SMA = short term moving average • LMA = Long term movie average • Trend improving, SMA will rise quickly than LMA. MACD line turn up • Trends losing strength, SMA tend to flatten, ultimately falling below LMA.

MACD • During price movement, SMA will move apart (diverge) and move together (converge) with LMA, hence, the name “Moving Average Convergence Divergence”

Length of moving averages • As a general rule, the longer term moving average will be two to three times the length of the shorter term average • The shorter the short term average, the more sensitive will MACD be to short term market fluctuations.

Signal line • The signal line is an exponential average of MACD levels, not of the price of the investment. • Signal lines are usually created employing 3-day to 9-day exponential averages of MACD lines. • The shorter the average the more sensitive will be the signal line.

Buy and Cell signal • Changes in MACD direction (from down to up and vice versa) and crossing of MACD lines above and below 0 carry significance. • Crosses of MACD from below to above its signal line and from above to below its signal line carry additional significance of their own.

MACD • As a general rule, crossing of MACD from below to above its signal line may be taken as confirmation of buy signals orignally indicated when changes in direction have taken places in MACD from down to up. • Signal line crossing take place after MACD lines change direction

Length of moving averages • As a general rule, the longer term moving average will be two to three times the length of the shorter term average • The shorter the short term average, the more sensitive will MACD be to short term market fluctuations.

Relative Strength Index • RSI is one of the most popular momentum indicator in Technical Analysis. • RSI value fluctuates between 0 to 100 and indicates the strength and velocity of price move

RSI • When the RSI move above 50, the average gains outweigh the average losses; this is regarded as bullish. • When the RSI falls below 50, the average losses outweigh the average gains; this is regarded as bearish.

RSI • RSI indicator is mostly calculated on 14 period timeframe and any value above 70 indicates over-brought level and value below 30 indicates over-sold level. • There are many variations of this indicator and the manner in which the RSI levels are interpreted varies with your trading style

RSI Calculation • It is a technical indicator used in analysis of financial markets. • RSI = 100 – 100/(1+RS*) • RS * =Avg of x days’ up closes/Avg of x days’ down closes

Combine Trading Signals • Machine Learning Methods • Classification Methods • Ensemble Methods • Deep Learning Neural Network • LSTM (Long Short Term Memory)

Thanks

www.algoji.com

ML for Trading

Dr. Hari, PhD. IISc Bangalore Nitish Mukherjee, Director, IIQF Date : 5th May 2018

Overview and content • Machine Learning (ML) • ML application to trading • Statistical Arbitrage • Linear Regression

• Limit Order Submission • Hidden Markov Model

• Market Order submission • Classification and Regression Methods

Sample ML problem setup

What are you trying to predict?

ML frame for predicting future price

Split Data into Training and Test Data

Split Data into Training, Validation and Test Data

Supervised v/s unsupervised learning

Regression v/s classification

ML Algorithms • Supervised • Linear Regression • Logistic Regression

• Unsupervised • Clustering Algorithm

• Semi-Supervised

Regression and Classification • Linear Regression • Logistic Regression • Decision Tree classification and Regression • SVM Regression and Classification • Deep Neural Network for Regression and Classification

Train and Optimize your model using Training and Validation Datasets

Backtest performance on (yet untouched) Test Dataset

Rolling Validation

Ensemble Learning

Bagging

Boosting

ML for Statistical Arbitrage • Developed in the 1980’s by a group of Quants at Morgan Stanley, who reportedly made over $50 million profit for the firm in 1987 • A contrarian strategy that tries to profit from the principles of meanreversion processes • In theory, one could expand the strategy to include a basket of more than a pair of related stocks

Main Idea • Choose a pair of stocks that move together very closely, based on a certain criteria (i.e. Coke & Pepsi) • Wait until the prices diverge beyond a certain threshold, then short the “winner” and buy the “loser” • Reverse your positions when the two prices converge --> Profit from the reversal in trend

Example of a Pairs Trade

Investor Decisions • Pair Selection Criteria • Correlation (Parametric & Non-Parametric Spearman’s Rho) • Dickey-Fuller Test Statistic (Cointegration) • Trading Threshold (areas of consideration) • Volatility of the Market • Historical returns • Cost of each transaction

Chevron & Exxon Formation Period Corr=0.93 Trading Period Corr=0.96 Optimal Threshold=1.25*sd’s # Transactions=10 Returns=15% Win.

Electronic Arts & GAP ◼ ◼ ◼ ◼

◼ ◼

Formation Corr=0.12 Trading Corr=0.56 Optimal Threshold=1 sd # Transactions=0 (Open a position, but spread never returns to 0) Return= -0.04 Lose.

Nike & McDonald’s      

Formation Corr=0.87 Trading Corr=0.02 #Transactions=1 Return= -0.05 Lose. Correlation is imperfect criteria for selecting pairs.

Cointegration • If there exists a relationship between two non-stationary I(1) series, Y and X , such that the residuals of the regression Yt =  0 + 1 X t + ut are stationary, then the variables in question are said to be cointegrated 55

X

Note: X and Y here are clearly not stationary, but they seem to move together. In fact, they are cointegrated --> (Y- β1X-β0 )should be stationary

Y

50 45 40 35 30 25 20 15 10 0

10

20

30

40

50

60

70

80

90

100

Application to Pairs Trading • If we have two stocks, X & Y, that are cointegrated in their price movements, then any divergence in the spread from 0 should be temporary and mean-reverting. Spread

time

• The important issues here are: 1) how to test for cointegration between prices and 2) estimating the constant

Testing For Cointegration • Many Methods – most of them focus on testing whether the residuals of stationary processes Yt = are 0 + 1 X t + ut • We use the Cointegrating Regression Dickey-Fuller Test, which essentially operates the following regression: Δut = φ ut-1 + et • H0: φ = 0

=> no cointegration*

• Ha: φ < 0

=>

cointegration*

• To obtain the cointegration factor estimates, we must regress the detrended Yt on the de-trended Xt * We must use critical values different from Gaussian ones due to non-symmetric properties of the Dickey-Fuller distribution

Trading Period Comparison

Normalized LUV&PLL spread VS Cointegrated LUV&PLL spread

Auto-Regressive Time Series • Cointegration is an ideal construct for pairs trading • But Dickey-Fuller Hypothesis Test is inconclusive • Instead we can fit a time series to the spread data • AR(1): Yt = β Yt-1 + εt • Looking for a spread that produces an AR(1) with will be stationary.

|β| < 1, so that

Alternative Strategies • Conditional correlation or some other measure of “relatedness”, such as Copulas • Modeling the spread as GARCH processes

Goog/MSFT Pair Trading

MMM/CSCO Pair Trading

CMCSA/CSCO Pair Trading

ETF Arbitrage • ETFs (Exchange Traded Funds) consist of a basket of stocks • If a trader has the correct amount of stocks, he can actually go to the ETF manager and exchange his stocks for an ETF. Likewise, if you own an ETF, you can go to the fund manager and redeem your ETF for the underlying stocks • The arbitrage opportunity occurs when there is a price discrepancy between the price of the ETF and the price of the underlying

ETF Arbitrage • Market making firms like Jane Street Capital dedicate significant resources to developing the most sophisticated hardware to exploit the tiny arbitrage opportunities that exist in this space.

ML for Limit Order Submission

Bid-Ask Spread • The order book is made up of two sides, Asks (also called offers), and Bids. Asks are people willing to sell, and bids are people willing to buy. • The best ask, the lowest price that someone is willing to sell at, is larger than the best bid, the highest price that someone is willing to buy at. • If this was not the case, a trade between these two parties would not have already happened. • The difference between the best ask and best bid is called the spread.

Market Order • Market orders will go into market to execute at the best available price, however the execution and the price is not guaranteed. • Market orders cannot be accepted outside of market hours or when trading in a particular stock is halted or suspended. • Market orders cannot be amended or cancelled online during market hours.

Limit Order • Limit orders allow you to set a maximum purchase price for your buy order, or a minimum sale price for your sell orders • If the market doesn't reach your limit price, your order will not be executed. • Limit orders can be amended or cancelled provided the order has not already been executed.

Limit Order Submission • Highly quantitative in nature • Limit orders are submitted by market makers • Opportunity from bid-ask spread • Probabilistic models • Hidden Markov models

Time Series Pattern Recognition • Stock data are time series data • Regression based methods like ARIMA, ARCH, GARCH models • DNN models like RNN and LSTM models for price series prediction or trend prediction • Ensemble learning like Bagging, Boosting for price or trend prediction

Thanks

Indian Institute of Quantitative Finance Post Graduate Program in Algorithmic Trading (PGPAT) • A comprehensive course covering all aspects of Algorithmic Trading: • Learn to develop advanced trading strategies • Developing advanced trading strategies using Technical Analysis, Machine Learning, Quantitative Techniques • Comprehensive Back-testing • Parameterization • Optimization • Coding Algorithmic Trading strategies in Python • Learn Money Management and Risk Management • Learn to set-up Algorithmic Trading infrastructure • Learn to develop and integrate your strategies with industry leading platform Omnesys NEST • Learn from the experts: Unparallel Expert Faculty Panel • Learn from expert faculty panel consisting of head of prop desks, pioneers of quantitative modelling in India, Ph.D’s from leading institutes like IISC Bangalore

• Duration: 6 Months • Schedule: Saturdays and Sundays • Fee: INR 85,000/- (All Inclusive) Email id: [email protected]

Phone No: +91-9769860151

www.algoji.com

Mean Reversion Trading Systems Presented by : Vishal Mehta , CMT

Million Dollar Question ?

Why Mean Reversion Trading Systems ❑Lasts for few hour to few weeks. ❑You Buy Low and Sell High (Comfortable for Traders) ❑Low Capital Deployment. ❑High Win to Loss Ratio . ❑Smooth equity curve. ❑Low drawdown.

Mean Reversion Trading Systems

Mean Reversion Trading Systems

Market Regime Bear Phase

Bull Phase

Bear Phase

Bull Phase

Sept’ 09- Dec ‘11 (Bear Phase)

Jan ‘11 – Feb’15 (Bull Phase)

Mar’15- Feb’16 (Bear Phase)

March’16 – Jan’18 (Bull Phase)

NIFTY

5476 - 4626

4650 - 8986

9006 - 7014

7236 - 11138

Point Drop/Gain

-850 Points

+4336 Points

-1992 Points

+3902

% Drop/Gain

-15%

93%

-22%

53%

RSI System

RSI System Performance Report Nifty Sept’09 – Jan ’18

5476 – 11138 = 5,662 Points

RSI System

+1228 Points

% Win

66%

% Time in Market

663 Days out of 3075 Day (21%)

Can I better this system ?

RSI System

RSI System Nifty Sept’09 – Jan ’18

5476 – 11138 = 5,662 Points

RSI System

+4497 Points

% Win

72%

% Time in Market

942 Days out of 3075 Day (31%)

Can I better this system even more?

RSI System

RSI System Performance

Nifty Sept’09 – Jan ’18

5476 – 11138 = 5,662 Points

RSI System 2

+5654 Points

Win

73%

% Time in Market

1087 Days out of 3075 Day (35%)

Too Much Drawdown ?

RSI System

RSI System Nifty Sept’09 – Jan ’18

5476 – 11138 = 5,662 Points

RSI System

+4034 Points

Win

83%

% Time in Market

491 Days out of 3075 Day (15%)

FAQ Q : If the strategy is so good why are you disclosing it ?

Q: What about Stop Loss ? Hedge – Options ? Q: What should I do while there are no trades ?

Q: You have not taken slippage, impact cost, Rollover cost etc.. ? Q: Do I need to have Algo to execute the same ? Q: Can I open the trade next day morning rather then closing ?

Thank You

Email : [email protected] Twitter : @vishalmehta29 Linkedin : Vishal Mehta, CMT

www.algoji.com

Strictly Private and Confidential

Strategy Development Snehal Soni Product Head- Algo Edelweiss Broking Limited

Introduction: Popular Approaches  Fundamental Analysis ➢ Top Down approach (EIC framework) ➢ Bottom Up approach (Stock Specific)  Technical Approach: Traditional Approach

➢ Trend Analysis: Trending & Non Trending ➢ Chart Analysis / Price Patterns ➢ Technical Indicators  Quantitative Approach

➢ Rule Based ➢ Statistical driven ➢ Mechanical Trading System

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Financial Markets: Efficient Market Hypothesis  Are variations in prices are just random noise ??

 Do they represent profitable trading opportunities ??  What categories of information are valuable to a trader ??

The various Levels  Strongly efficient : Even Insider is discounted by Prices  Semi – Strong Efficient: Public Information / Fundamentals etc

 Weakly Efficient : Historical Prices & Volume Information

“I’d be a bum with a tin cup in the street if the markets were efficient”…. Warren Buffet May 13, 2018

121

Financial Markets: Overview  Financial markets are non-stationary time series / Auto

correlated / Fat tails etc  Designed to recognize, exploit patterns that precede profitable

trading opportunities  As trader develop and trade models, inefficiencies that model

identify are removed from the market they trade  Characteristics of market change over time in part because

trading system makes them more efficient  Trading System that once worked will fail

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Strategy Development: Data  Data  Clean and Reliable: Basic level Check  Issue with Cash Data: Adjusted for bonus, stock-splits etc  Issues with Derivative Data : Expiry date

 Duration: Intraday or EOD  Free Sites like : finance.google.com, finance.yahoo.com  Division of Data into in-sample and out-of-sample data

 In-sample date: Optimization  Out-sample Data: Validation May 13, 2018

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Trading System Overview Trading system should jell with your psychology  First Step: Design a template of a trading System that

includes the most important and relevant features Three Step Approach  First Step

 Define Objective Function: Single Score  Trading Frequency: High, Medium or Low  Order Style: MOO, MOC, Limit Vs Market Order

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Trading System Overview Second Step  Trading account : Starting Balance Size  Type of Position: Long only ; Short only ; or Both

 Issues Selection: Equity, Commodities, FO etc  Leverage  No of position which will be open simultaneously

 Margin usage  Risk Assessment: % of trading capital on any single position

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Trading System Overview Third Stage  Entry & Exit Conditions (Profit / Stop Loss)  Backtesting : In-sample data & Out Sample Data  Optimization : Brute Force Optimization  Validity Test using Run Test

Post Validity, Go live on Pilot basis before going full fledged live

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Risk Management Risk Management… Is the risk worth taking ???...in the long run ?? Hit Rate = 35 % , 25%

Loss Rate= 65% , 75% Risk : Reward = 1 : 3 Expectancy (1) = 3 X 0.35 -1 X 0.65 = 1.05-0.65 = 0.40

Expectancy (2) = 3 X 0.25 -1 X 0.75 = 0.75-0.75 = 0.00  KEY IS RISK MANAGEMENT & not HIT RATE

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Money Management How much money you want to risk per trade ?? ……..in one’s trading a/c ?? in one day ?? Simply put.. How do you trade that minimizes your loses.. Thru …Hard Stops…Rolling Stop Loss.. Position sizing Hit Rate = 35 % Loss Rate= 65% Risk Capital per Trade: 1 % Total No of trades : 100 Probability= (0.65)^100 =1.9X10^-19  Probability has a multiplicative effect not additive  Easy to say….Difficult to implement May 13, 2018

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Money Management: Paradox…  The more you have …

…....the less risk you take

Everyone knows its importance.. ……few actually implement it This time its different… …..this is a big one…

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Money Management: Approach..  It is all about Taking small loses …but large profits  Capital Preservation Vs Capital Appreciation

 Lose battles ….but Win war  Live today to trade the next day..  Never overleverage  Loose only that much you are comfortable with…  Plan your Trade and trade your Plan

 Never make plans on the fly…No Impulsive trading

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Risk & Money Management….

Simply Speaking… Risk Management is everything you do before you take a trade

Money Management is Everything you do after you take a trade

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Trading System: Quantitative Approach Set of well defined Rules / condition which generate Buy / Sell Signals. It include 1. Entry Rules (Limit / Market / MOO/ MOC)

2. Exit Rules (Bullet / Bar-bell ) 3. Stop Loss Rules (Hard Stop Loss / Trailing Stop Loss) 4. Money Management Rules (Risk Capital Per Trade) 5. Risk Management Rules 6. Contingent Variables / Filters etc

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Trading System: Signal Generation Price of any tradable instrument at any given time can be categorized into  Trending  Trading Range

Accordingly, we have two types of trading systems 1. Trend Following Systems 2. Mean Reverting Systems

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Trading System : Trending Following System : Inertia Based on premise that prices continue to move in one direction for

a long period of time; one can recognize that trend has begin; take a position in the direction of trend to profit from it Advantage : Have Build in Stop-Loss Feature Example of Trend following systems: Moving Average Cross-Over System  Breakout System  Adaptive Moving Average System

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Moving Average Crossover System Takes position when the fast moving average crosses

Slow Moving Average from below or top Buy Condition: Fast MA cuts from below and rises

above Slow MA Sell Condition: Fast MA cuts from above and

penetrates Slow MA Always in the market Strategy wherein Buy and Sell

automatically liquidates previous position and takes fresh position

May 13, 2018

Adaptive Moving Average System Concept: Noisy market requires a slower trend than a

quite trend. Moving Average should lag further behind a noisy

market to avoid whipsaws Approach: Use Fast MA in trending market and Slow

MA in noisy / congesting market Example:

KAMA (Kaufmann

Adaptive

Moving

Average System)

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Break-Out System 1. Model 1 :  Buy the close if close is higher than open

 Sell the close if close is lower than open 2. Model 2: Bollinger Band  Buy as the Price rises through the upper Bollinger band

 Exit when the Price drops into the Bollinger Band

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Break-Out System 3. Model 3:  Buy when high rises above previous high of 20 (n)

days  Sell when low drops below the previous low of 10 (n)

days  Set a maximum loss stop based on Volatility (possible

using ATR)

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Mean Reverting System (MRS)  MRS are based on premise that price tends to oscillate

above and below (market determined) a level of equilibrium with some degree of regularity  Fails badly when in trend as the system is designed to take

position against the trend

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Mean Reversting System (MRS)  Reversion trading does not mean buying when price rallies

or selling when it drops;  One needs to wait for a confirmed shift in price and only

when clear signals that the balance between bulls and

bears has shifted ,one enters the traders  Patience is a big part of being a mean reversion trader and

one has to be on the sidelines most of the time

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Mean Reversion System (MRS)  Must have a filter to identify whether the market is

Trending or Non Trending Mode Models 1: Anticipating Mean Reversion  Buy after three down days

 Sell after three up Days

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Mean Reversion System (MRS) Use following types of tools  Oscillators  Mean Reverting Indictors  Overbought/Oversold Indicators  Sentiment

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Adaptive Trading Models: Best of Both.. Use following types of tools  Stand-alone Trend Following Models  Stand-alone Mean Reverting Model  Strength of Trend composite to determine whether

market is trending or churning sideways  Diffusion Models based on signals from ADX,

Efficiency Ratio, R2 and Bandpass filter Indicators

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Adaptive Trading Models

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Adaptive Trading Models : Popular Tools  Wells Wilder’s ADX  Perry Kaufman’s Efficiency Ratio’s*  Linear Regression:

Correlation coefficient value (r2) is the result of measuring the residuals of a linear regression. Linear regression fit is strong, r2 will be near 1, indicating a trending market. Linear regression fit is weak, r2 will be near 0, indicating a directionless market.

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Adaptive Trading Models : Popular Tools  Wells Wilder’s ADX: The Trend Strength Indicator

 ADX is used to quantify trend strength and is non -

directional.  ADX calculations are based on a moving average of

price range expansion over a given period of time.

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Adaptive Trading Models : Popular Tools  Low values is usually a sign of accumulation or

distribution  Period of Low ADX leads to various price patterns

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Adaptive Trading Models : Popular Tools  Perry Kaufman’s Efficiency Ratio’s:

 Measures the ratio of the relative market speed in

relation to the volatility.  Used as a filter to help avoid trading when the market is

“choppy” or flat ranging markets.  Use to identify smoother market trends.  Value oscillates between 0 and 1, where higher values

represents trending market

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Potential Model Enhancements  Use of Filters

 Multiple time frame  AD Ratio  Volume Filters, buy or sell only  Improve optimization techniques to choose strength of

Trend Indicators  Look for additional indicators to help determine market

environment (Janus Factor, Performance of Quant Factors) May 13, 2018

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Points to Ponder !

 Avoid working with point difference rather work with

ratios or percentages  Aware of structural changes  Manual Vs Program Trading  Greater no of shares being traded today– AD no’s

cannot be compared  Normalize the data using ratios  Deviation from Trend

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Thank You

Contact for more details: Snehal Soni Edelweiss Broking Limited, First Floor, Tower -3, Kohinoor, Kurla (W), MumbaiEmail: [email protected]

www.algoji.com

Fixed income Trading strategies global markets Chintan thakkar – 5th may 2018

Global exchanges

bonds • Fixed Income Instruments • Bond Auctions • Bond Pricing – Present Value of it’s cash flows • Measuring Risk • Macaulay Duration • Modified Duration

• Key Characteristics • Type of Issuer

• • • •

Term to Maturity Principal Coupon Rate Coupon Frequency

Yield curve

Cme group interest rate products 3.50%

Ultra Long Bond

Eurodollars 0-10 Years 3.00%

Classic Long Bond

30-Year MAC

2.50%

Ultra 10-Year T-note 2.00%

10-Year T-note 5-Year T-note

1.50%

2-Year T-note

1.00%

20-Year MAC

10-Year MAC

7-Year MAC

5-Year MAC

CME Group Interest Rate futures include Fed Funds, Eurodollars, US Treasury, and Swap based products.

2-Year MAC 0.50%

30-Day Fed Funds; 0-3 Years 0.00%

0

2

5

7

10

20

30

Treasury futures – contract specifications 2-Year T-Note Futures

5-Year T-Note Futures

10-Year T-Note Futures

Ultra 10 Futures

T- Bond Futures

Ultra T-Bond Futures

Face Amount

$200,000

$100,000

$100,000

$100,000

$100,000

$100,000

Deliverable Maturities

1 3/4 to 2 years

4 1/6 to 5 1/4 years

6 1/2 to 10 years

9 5/12 to 10 Years

15 years up to 25 years

25 years to 30 years

Contract Months

March quarterly cycle: March, June, September, and December

Trading Hours

Monday - Friday; Electronic: 5:00 pm - 4:00 pm, Sunday - Friday (Central Times)

Last Trading & Delivery Day

Last business day of contract month; delivery may occur on any day of contract month up to and including last business day of month

Minimum Tick

In percent of par to In percent of par to one one-quarter of 1/32nd quarter of 1/32nd of of 1% of par 1% of par

Minimum Tick Value

$15.625

$7.8125

Day prior to last seven (7) business days of contract month; delivery may occur on any day of contract month up to and including last business day of month

In percent of par to one-half of 1/32nd of 1% of par

In percent of par to one-half of 1/32nd of 1% of par

In percent of par to 1/32nd of 1% of par

In percent of par to 1/32nd of 1% of par

$15.625

$15.625

$31.25

$31.25

Yield curve trading strategies • • • • •

2 Year – 5 Year Spread 5 Year – 10 Year Spread 2 Year – 5 Year – 10 Year Butterfly 5 Year – 10 Year – 30 Year Butterfly And many more Spread and Butterfly Combinations

Constructing a yield curve trade • Note: The DV01 or Duration of a Longer dated bond is higher than a shorter

dated bond • I.e. The dollar value change for a 1% change in interest rate change is higher for a 10 Yr bond than a 5 Yr bond • Yield Curve Trade requires DV01 to be equalized (or close to equalized) • How do we calculate the appropriate hedge ratio?

Treasury futures analytics tool

Calculating 2 Year – 10 year spread • The shorter maturity bond is usually taken as the first leg of the trade and longer

maturity bond is the second leg • Price calculation formula = 2 Year T-Note x 208 x 2 minus 10 Year T-Note x 100 • = 107-217 x 208 x 2 – 124-270 x 100 • Note: 2 Year T-Note is multiplied with 2 since the underlying for this contract is $200,000 which is twice that of a 10 Year T-Note which is $100,000

Treasury futures analytics tool

Calculating 2 Year – 5 year – 10 year butterfly 2 Yr T-Note 5 Yr T-Note 10 Yr T-Note 126

100

2s5s Spread

163

100

5s10s Spread

15

24

8

Matching 5 Yr Leg

2

3

1

Smaller Ratio

• In a butterfly trade, a trader buys or sells the near spread i.e. 2s5s and does the opposite to the far spread i.e. 5s10s. Assume that trader buys the 2s5s spread • (+ 2 Year T-Note – 5 Yr T-Note) – (5 Yr T-Note – 10 Yr TNote) • 2 Yr T-Note x 2 x 2 – 3 x 5 Yr T-Note + 10 Yr T-Note • 107225 x 2 x 2 – 117060 x 3 + 124300 x 1

Advantages of trading spreads and butterflies

Multiple Intraday opportunities

No direct exposure to interest rates

Low risk per trade Relatively lower margins and greater capital efficiency

Trades in an identifiable range

What can move these strategies?

Geopolitical events

Federal Reserve Meetings

FOMC Rate Decisions

Retail Sales

GDP & Inflation

Jobs Report (NFP)

Thank you CHINTAN THAKKAR [email protected] [email protected]

www.algoji.com

Experiences Deepak Pundir Algo Head, FinDoc Automated Algorithm / HFT/Quant Rule Based Algo / AI Based Algo (C,C++,C#, R, Python, AFL)

Types of market strategies

Statistical Arbitrage

Intraday Algos

Positional Algos

Statistical Arbitrage This type of strategies are basic arbitrage strategies such as cash to future, Inter-exchange & triangular international arbitrage. A). Future-Future B). Conversion – Reversion C). Delta Hedging strategies(IV, IV Spread) D). Cash – Future E). Butterfly, Pair Scalping, Option Ratio etc. PRACTICAL ISSUES A). Rapid Strategy Implementation B). Need best Infrastructure C). Internal Order Matching D). New guide line from exchange.

Intraday Algos This type of strategies involve the Market Making, HFT , LFT, Quant based algo ,AI , ML and other momentum play for intra day churning most of the strategies in this segment involve fund for very less time. The greatest portion of present day algo-trading is high frequency trading (HFT), which attempts to capitalize on placing a large number of orders at very fast speeds across multiple markets and multiple decision parameters, based on pre-programmed instructions.

PRACTICAL ISSUES A). The coding and flow of strategies. B). Need best Infrastructure C). Data Crunching for best trading parameters.

Positional Algos These system has great advantage of being able to take the emotions of greed and fear out of trading and ensure profitability over time in different market conditions. This is a High Risk and High Return Strategy. These type of strategies involve long term period investment based on the indicator derived from a algo that generates signals and execute them automatically. Base of all models are Mathematical Model, Statistical Analysis, Optimization Techniques, Technical Analysis, AI,ML. PRACTICAL ISSUES A). Proper back testing. B). Proper money management system according to algo. C). Reliable Trading Infra.

various returns over the period of last 5 year. Types Of Algo

Annualized Return

Max Drawdown

Infra cost

20- 25%(approx)

NILL(approx)

Higher Infra Cost

40-45%(approx)

5-10%(approx)

Higher Infra Cost

50-60%(approx)

15-20%(approx)

Less Infra cost

Step 1: Statistical Arbitrage Step 2: HFT/LFT/Quant Based

Step 3:

Positional Algos

[email protected]

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Algorithmic Trading Crypto-currencies Garv Khurana Institute and Faculty of Actuaries

Cryptocurrency Features (Trading Perspective) • Similar to Forex markets • crypto-fiat, crypto-crypto pairs • Less trade regulations • Markets are weak—form • • • • •

inefficient Highly volatile Ease of transfer Transparent risks Increasing market cap Growing market places ( New-age platforms)

Exhibit 1- One Pair Arbitrage

Exhibit 2 - Three Pair Arbitrage

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INDIAN TURTLES Presented By

Mayur Sampat Mumbai Algo Traders Conference

5th May 2018

Mayur Kishore Sampat, am full time Trader, Coach & Mentor, I am not a former market maker or specialist or a licensed professional in the financial industry, yes, I have done Technical Trading courses form BSE Institute to start with, then became a Online Trading Academy Grad., NISM Certified Forex, Capital Market & Derivative & Investor Advisor level 1.

However to remind you that I am too a Retail Investor just like you, who found a way to be successful and profitable trading stocks like a real 'business; from my home office. By profession Computer Engineer, PDCME, DBM was into Computer Hardware Business for almost 20 plus years, before I accidently took trading stock market as a new Business.

ONE QUESTION YOU ASK YOURSELF

ARE WE TAKING MONEY HOME ?

Quarterly Results • Maruti with Profits went down 2.18%

• Axis Bank with Losses went up by 9.21%

ALGO Based trading helps removes emotions

DO YOU HAVE A TRADE PLAN

THAT’S WHERE THE ALGO BASED TRADING HELPs

MARKET IS ONLY ABOUT RISK N RISK MANAGEMENT

S u c c e s s f u l Tr a d e r s F o c u s o n R i s k Algo based trading helps in risk management

EVERY DAY WE ARE IN MARKET

YOU CAN NOT BE WHAT YOU WANT TO BE’ IF YOU KEEP REPEATING THE SAME THING EVERYDAY & “NOT LEARNING FROM YOUR PAST MISTAKES” ‘

Carry home points ❑ REMEMBER EVEN IF YOU ARE NOT THERE

THE MARKET REMAINS ❑ YOU CAN ONLY REMAIN ALIVE IN MARKET BY PROTECTING YOUR CAPITAL ❑ DO NOT OVER TRADE ❑ DO NOT BE GREED & BLAMES OTHERS ❑ DO NOT DEVIATE FROM YOUR PLAN OF ACTION (TRADE PLAN) ❑ TAKE BABY STEPS INITIALLY AND WORK YOUR WAY TO TOP

Winning Attitude Requires CLARITY FOCUS DEDICATION TARGET

THANK YOU MAYUR SAMPAT [email protected]

www.algoji.com

Santosh Kumar Pasi santoshpasi.blogspot.com 197

Profile Santosh Kumar Pasi • IT Securities professional • 21 Years experience in IT • 12 Years experience in trading (Stock, Futures, Commodities, Forex and Options) • Currently managing “OptionsOracle India Plugin” • Developed application “OpStrater” • Worked/Travelled in India, USA, UK, Australia, Singapore and Sweden

santoshpasi.blogspot.com

198

Trading - Focus Probability

Profitability

Greek

Position Size

• Chances of winning

• Risk to Reward Ratio

• Greek should be reasonable, under control

• Controlled position size santoshpasi.blogspot.com

199

Algo Trading You can delay it, but can’t be ignored

Better adapt now and get advantage of early-bird entry

Emotionless, but systematic trading

Keep evolving

santoshpasi.blogspot.com

200

Thank you . • Thank you for your attention

santoshpasi.blogspot.com

201

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SIMPLE YET EFFECTIVE Sachin Sarvade

• A hammer is a type of bullish reversal candlestick pattern, made up of just one candle, found in price charts of financial assets. The candle looks like a hammer, as it has a long lower wick and a short body at the top of the candlestick with little or no upper wick. • SOURCE- WIKI

THANK YOU •SACHIN SARVADE •RESEARCH ANALYST- TECHNICAL •EMAIL ID- [email protected]

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Thank You [email protected]

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5th May, 2018