alpha matters quant research jun2014
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Quant Research
APR 2014
Introducing AlphaMatters Quantitative Research services and
products for market participants
Proven offerings in
Key Expertise
the market
in:
Trend / Mean Reversion / Stat Arb modeling;
Complete trading system design
Proprietary and Confidential ALPHAMATTERS 2
Core Team
Ganapathy Subramanian: 20+ years in the start up ecosystem as an investor, mentor and entrepreneur; Engg from NIT (Trichy), MBA from XLRI Bharath Rao: 10+ years of experience in analytics, product development and marketing; Engg from RV College of Engineering (Bangalore), MBA from University of Georgia; Led Developer Marketing in IBM and held senior positions in many startups Hemanth Kumar: 8+ Years of Research Experience in Image Processing, Speech Recognition, Computer Vision and Artificial Intelligence; Credited with 3 patents; Engg from PESIT (Bangalore) Completing PhD at the Indian Institute of Science
IndexMatters:(Index trading system)
Comprehensive intraday
trading system for NIFTY (Indian Equity Index) from
entry till exit
Annualized yield:
~37%
Can be used as an alpha
generating input for hedged strategies or as a Decision
Support System for intraday
hedging the Indian index Nifty
Distributed through
Thomson Reuters and brokers in the Indian
market
Instruments Nifty Futures (Current Month)
Net Winners’ Ratio 59%
Performance Metrics of IndexMatters
Time Period 2 years
Frequency of trades ~ 1.6 per day
Profitability (Post Transaction) 0.09% per trade
Max Drawdown 2.6% of Capital
Sharpe Ratio 2.9
Annualized Return 37%
Transaction Cost Assumed 0.06% per trade
Leverage Assumed 1x
Equity Curve of IndexMatters
500000
700000
900000
1100000
1300000
1500000
1700000
1900000
2100000
1 14
27
40
53
66
79
92
10
5
118
13
1
144
157
17
0
183
19
6
209
22
2
235
24
8
261
274
28
7
300
31
3
326
33
9
352
36
5
378
391
40
4
417
43
0
443
45
6
469
48
2
Cumulative Capital vs Time in Days
Capital
CommodityMatters
Short Term
Momentum based
trading system for
agri commodities
Annualized Yield: ~29%
Developed for Olam
International
Can be used as DSS for hedging
Decisions
Instruments Corn, Wheat, Soy Futures (2nd Month Continuous)
Net Winners’ Ratio 48%
Performance Metrics of CommodityMatters
Time Period 4 Years
Frequency of trades ~ 1 per day
Profitability (Post Transaction) ~ 0.1% per trade
Max Drawdown 14% of Capital
Sharpe Ratio 1.7
Annualized Return 29%
Transaction Cost Assumed 0.03% per trade
Leverage Assumed 1x
Instruments Corn, Wheat, Soy Futures (2nd month Continuous)
Net Winners’ Ratio 48%
Performance Metrics of CommodityMatters
Time Period 4 Years (Feb 2010 ton Jan 2014)
Frequency of trades ~ 1 per day
Profitability (Post Transaction) 0.09% per trade
Max Drawdown 12% of Capital
Sharpe Ratio 1.7
Annualized Return 29%
Transaction Cost Assumed 0.03% per trade
Leverage Assumed 1x
Equity Curve of CommodityMatters
500000
1000000
1500000
2000000
2500000
3000000 1 30
59
88
117
146
175
204
233
262
291
320
349
378
407
436
465
494
523
552
581
610
639
668
697
726
755
784
813
842
871
900
929
958
987
Cumulative Capital versus Time in Days
OptionMatters
Medium Frequency
Stat-Arb on Index
Options
Yield Per Trade: 20 bps
Learns and uses the lack of parity between puts and calls
Number of Trades per Day: ~ 50
Winning Ratio: 88%
Instruments Corn, Wheat, Soy Futures (2nd month Continuous)
Net Winners’ Ratio 48%
Performance Metrics of CommodityMatters
Time Period 4 Years (Feb 2010 ton Jan 2014)
Frequency of trades ~ 1 per day
Profitability (Post Transaction) 0.09% per trade
Max Drawdown 12% of Capital
Sharpe Ratio 1.7
Annualized Return 29%
Transaction Cost Assumed 0.03% per trade
Leverage Assumed 1x
Equity Curve of OptionMatters
0
200000
400000
600000
800000
1000000
1200000
40599 40675 40745 40820 40897 41122 41197 41271 41341 41417
CU
MU
LATI
VE
EQ
UIT
Y
TIME
Cumulative Capital vs Time in Days
Proprietary Algorithms
Feature Selection Algorithm to
identify the early and confirmatory indicators (like
implied vol, global markets, broader
markets, high beta indices, currency
etc.)
An advanced classifier built on
top of SVM, trained on 200k
samples & tailored to financial markets to
recognize when “random walk”
breaks
Intelligent filter designed to cut
the market noise and identify 2 to3
real trends / reversals in a day and trail the trends
Tools
Algos
• Stat Arb Pa
rad
igm
s
• Mean Reversion
• Trade Execution
• Factor Models
• Trend Following
Too
l E
xp
ert
ise
• Matlab / R / Python,
• Excel / VBA,
• PHP
• MySQL
• C / C++ / Java
Ma
th E
xp
ert
ise
• Time Series Modelling
• Multivariate Regression
• Neural Networks
• Support Vector Machines
• Decision Trees
• Markov Chains
• Monte Carlo Simulations
Support Services
Back Testing
Hypotheses Testing &
Risk Modeling
Data Preparation / Validation
ETL Development
& Preprocessing
Tool / Indicator
Development
Compliance & Quality Assurance
Customers / Partners
Financial Information providers
Broking firms
Advisory
service provider
Hedge Funds
Collaborate in Developing Algo Trading / Decision Support Systems;
License our IP;
Customize our IP to different markets;
Leveraging us
