2) Investment Program - 20 Sept 2016
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1 | Page x INVESTMENT PROGRAM EARNINGS SENTIMENTS ALGORITHM Last Updated on 23 rd March 2016
Transcript of 2) Investment Program - 20 Sept 2016
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x
INVESTMENT PROGRAM EARNINGS SENTIMENTS ALGORITHM
Last Updated on 23rd March 2016
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Contents Page
Contents Page ........................................................................................................................................................ 2
The Opportunity .................................................................................................................................................... 3
Reasons to Invest Now .......................................................................................................................................... 4
A Unique Model ..................................................................................................................................................... 5
Strategy Features (both MFT & HFT ESA algorithms) ............................................................................................. 6
Earnings Sentiments Algorithm – MFT Strategy ..................................................................................................... 7
Performance Results 2007-2015 ............................................................................................................................... 7
Portfolio Data ............................................................................................................................................................ 8
Strategy Chart Descriptions ....................................................................................................................................... 9
Earnings Sentiments Algorithm – HFT Strategy .................................................................................................... 10
Performance Results 2007-2015 ............................................................................................................................. 10
Portfolio Data .......................................................................................................................................................... 11
Strategy Chart Descriptions ..................................................................................................................................... 12
Other Algorithms in R&D ..................................................................................................................................... 13
Bollinger Band Algorithm ........................................................................................................................................ 13
Technical multi-indicator Momentum Algorithm ................................................................................................... 13
Risk Management ................................................................................................................................................ 14
Operations ........................................................................................................................................................... 15
Trading Process .................................................................................................................................................... 16
Scalability ............................................................................................................................................................ 17
Investment Terms ................................................................................................................................................ 18
Contact Information ............................................................................................................................................ 19
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The Opportunity
Participate in the forefront of Artificial Intelligence (AI)
based algorithmic trading
Our Sentiment-scoring algorithms are created using
advanced techniques in Machine Learning and Genetic
Optimization
Average compounded annual return over >35% (net of
fees; 2011-2015)
Additional advanced sentiment algorithms in research and
development phase and will be deployed in the future
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Reasons to Invest Now
Limited partnership restricted to 25 accredited or qualified
investors
Higher minimum investment in Units of USD $100,000
starting in 2017
Trading capacity of the Earnings Sentiments Algorithm
(ESA) is USD $17 Million (and scalable, refer to page 10)
Join the EquitySoft Team as a Valued, Initial Investor
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A Unique Model
Our Founder and CEO’s research has allowed EquitySoft to develop a self-
adaptive algorithm that produces consistently high returns on investment, being
able to auto-adapt to differing market conditions and periods of market volatility
with low downside risk.
The current problem with the lifetime of most algorithms and investment/trading
strategies is that these systems are static pricing models or variance-smoothing
strategies that require manual adaptation of parameters in the model to maintain
the profitability of these strategies. No matter the experience of the fund manager,
human error, greed and fear, and human discretion are still subject to uncertainty.
This limitation has been so profound that some hedge funds have a typical lifetime
of 5 to 8 years because their strategies are not able to adapt to the changing
financial and economic landscape.
We recognize that predicting price movements is becoming more and more
difficult due to the increased dominance of trading algorithms. Rather than
developing a static model like other financial institutions and professionals,
EquitySoft has developed a machine-learning algorithm that uses adaptive and
evolutionary mathematics to learn the significance of performance values to
computationally self-adapt to and exploit ever-changing sentiments in both the
financial and macro-economic environments. By allowing an algorithm to self-
learn historical data, the strategy can self-detect quantitative patterns in market
conditions and adapt rapidly with high accuracy without human intervention –
which can be erroneous, biased, and emotional. Our algorithm is reliable as it
evolves its pricing and quantitative predictive mathematics with the evolving
financial-economic environment, especially during both economic crises and
rallies.
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Strategy Features (both MFT & HFT
ESA algorithms)
A. Regression Model
B. Data Features
Sentiment Factors
Fundamental
Performance Factors
Macro-Economic
Factors
Error and Threshold
Constraints
EquitySoft uses a machine-learning method that is a proprietary modified version of the
Generalized Additive Models Trees method. This provides for a healthy balance between
interpretability of trade signals and flexibility of the model to its training data.
EquitySoft uses a combination of sentiment and fundamental factors to gain greater insights
into investor sentiments and macro factors to account for external factors to the company.
Other statistical calculations are used to ensure high precision of trade predictions.
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Earnings Sentiments Algorithm –
MFT Strategy
Performance Results 2007-2015
Initial NAV $1,000.00
Gross Profit 1576.26%
Gross Loss -451.92%
Net Profit $4,206.23
Final NAV $5,206.23
Average Annual Return After Fees 20.1%
Profit Factor 14.9%
# Days 877
# Winning Days 644
Daily Win Rate 73.43%
Best Month 13.54%
Worst Month -1.54%
Best Day 5.42%
Worst Day -3.90%
Monthly Win Rate 91.43%
Pre-fee Performance over 4 years 480.52%
Average Annual Gross Return 21.58%
Annual Volatility 7.95%
Semi-Deviation 36.08%
Skewness on monthly return 1.50
Kurtosis on monthly return 4.58
Sharpe Ratio 1.42
Sortino Ratio 0.31
Largest 1-Day Loss % -3.90%
Max Drawdown% (monthly) -42.48%
Max Drawdown% (daily) -14.64%
Return / Max Drawdown 3.37
Average Monthly Turnover n/a
Average Holding Period (mins) Max 1 day hold
Calmar ratio 0.51
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Portfolio Data
Portfolio Analysis
Backtested Monthly Returns
Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Jan Annual Return
Standard Dev
Sharpe Ratio
Max Drawdown
2007-2008 0.0% 0.0% 0.0% 0.0% 0.0% 3.2% -1.5% 0.0% 3.6% -0.1% 0.0% 13.5% 18.2% 13.5% 0.59 -36.2%
2008-2009 4.4% 0.0% 0.6% -1.2% 0.0% 2.5% 2.4% 0.0% 4.8% 0.0% 0.7% 1.5% 15.2% 6.5% 0.77 -42.5%
2009-2010 5.5% 0.0% 6.6% 2.9% 0.0% 2.0% 0.1% 0.0% 7.1% 2.1% 0.0% 1.4% 29.5% 9.3% 2.07 -32.3%
2010-2011 2.2% 0.0% 4.9% 0.1% 0.0% 3.1% 0.5% 0.0% 0.3% 0.8% 0.0% -0.1% 11.2% 5.5% 0.16 -15.5%
2011-2012 0.9% 0.0% 0.2% 1.3% 0.0% 6.1% 3.1% 0.0% 0.4% 0.0% 0.0% 3.9% 15.4% 6.9% 0.74 -17.6%
2012-2013 3.9% 0.0% -0.6% 0.1% 0.0% 2.6% 0.0% 0.0% 2.1% 2.4% 0.0% 2.4% 12.4% 5.2% 0.41 -17.6%
2013-2014 2.2% 0.0% 2.5% 4.9% 0.0% 7.8% 0.0% 0.0% 4.0% 0.0% 0.0% 4.6% 27.3% 9.1% 1.86 -22.4%
2014-2015 1.5% 0.0% 3.6% 1.3% 0.0% 6.1% 0.5% 0.0% 0.6% 2.4% 0.0% 3.4% 19.8% 6.7% 1.43 -24.7%
2015-2016 3.8% -0.7% 1.6% 1.6% 0.0% 5.2% 3.0% 0.0% 6.4% 0.9% 0.0% 3.4% 26.5% 7.9% 2.06 -17.5%
2016-2017 0.9% 0.0% 1.0% 2.9% 0.0% 1.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 18.2% 13.5% 0.59 0.0%
Strategy Description
The medium-frequency-trading ESA
(Earnings Sentiments Algorithm) is a low
latency, news-driven long/short strategy
that trades sentiments on company
earnings releases. ESA uses proprietary
evolutionary optimization and machine-
learning mathematics to self-adapt to the
changing financial-economic environment
so that eligible investors can experience
consistently strong capital returns on
investment.
Highlights
Fully automated algorithm
Intraday portfolio turnover
Fee Structure:
2016-2017
2017-2018
2018-2019
2019-onwards
Performance Fee 10% 15% 20% 20%
Management Fee 1% 2% 2% 2%
Operations Portfolio Instrument Selection Criteria
Excel API on multiple CPUs forms basic
architecture for medium-frequency
algorithm automation in terms of account
management, trade signals & watchlist,
and model & execution optimization.
Immediate improvements include Python
implementation for speed enhancement.
Operational costs are estimated at
USD$20,000 annually.
Currently NYSE and NASDAQ securities
Instrument’s 52-week average daily trading value must equal or
exceed USD$10 million
Minimum large market capitalization of USD$9 billion
Watchlist scalable to est. 201 instruments in NYSE & NASDAQ
Key Criteria: Correlation profile minimum of 70%
Top 10 traded equities
Xilinx Inc
International Flavors & Fragrances Inc
C R Bard Inc
Symantec Corp
CMS Energy Corp
Stanley Black & Decker Inc
Masco Corp
Royal Caribbean Cruises Ltd
Weyerhaeuser Co
Autozone IncHarley-Davidson Inc
Equity Style
Market Cap
Large
Medium
Small
Value Blend Growth
Growth Strategy
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Strategy Chart Descriptions
Highest Gross Profit: 13.54% Lowest Gross Loss: -4.32% Average Gross Profit: 1.94% Average Gross Loss -0.36%
Profitable Trades
Losing Trades
Win Ratio
Jan 59 -12 83% Feb 53 -16 77% Mar 0 -1 0% Apr 67 -32 68%
May 35 -10 78%
Jun 0 0 0%
Jul 94 -29 76%
Aug 23 -11 68%
Sep 2 -1 67%
Oct 83 -28 75%
Nov 27 -5 75%
Dec 1 0 100%
Profit Loss Win Ratio
2007 20 -5 80% 2008 48 -16 75% 2009 51 -14 78% 2010 33 -18 65% 2011 28 -6 82% 2012 42 -19 69% 2013 51 -14 78% 2014 61 -18 77% 2015 61 -14 81% 2016 32 -15 68%
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Earnings Sentiments Algorithm – HFT
Strategy
Performance Results 2007-2015
Initial NAV $1,000.00
Gross Profit 2534.32%
Gross Loss -179.24%
Net Profit $345,860,502.46
Final NAV $345,861,502.46
Average Annual Return After Fees 312.5%
Profit Factor 34.2%
# Days 1,770
# Winning Days 1,411
Daily Win Rate 79.72%
Best Month 119.85%
Worst Month -0.07%
Best Day 30.06%
Worst Day -0.99%
Monthly Win Rate 98.23%
Pre-fee Performance over 4 years 42347408.96%
Average Annual Gross Return 321.89%
Annual Volatility 59.24%
Semi-Deviation 2.09%
Skewness on monthly return 2.98
Kurtosis on monthly return 13.81
Sharpe Ratio 5.26
Sortino Ratio 149.43
Largest 1-Day Loss % -0.99%
Max Drawdown% (monthly) -4.08%
Max Drawdown% (daily) -2.04%
Return / Max Drawdown 25.66
Average Monthly Turnover (No overnight hold)
Average Holding Period (mins) 5
Calmar ratio 78.97
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Portfolio Data
Portfolio Analysis
Backtested Monthly Returns
Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Jan Annual Return
Standard Dev
Sharpe Ratio
Max Drawdown
2007-2008 1.3% 0.2% 0.1% 0.0% 0.8% 41.2% 3.1% 0.2% 24.0% 8.8% 3.2% 28.1% 158.7% 47.1% 3.16 -0.9%
2008-2009 15.2% 0.4% 29.1% 3.3% 0.5% 36.8% 18.4% 0.7% 72.8% 8.9% 0.5% 62.3% 658.5% 86.7% 7.48 -1.8%
2009-2010 43.2% 3.7% 119.8% 5.5% 1.1% 42.7% 16.0% 1.0% 18.9% 18.4% 1.7% 17.7% 856.5% 116.2% 7.29 -2.5%
2010-2011 12.2% 1.0% 19.3% 3.4% 2.0% 42.2% 2.0% 1.8% 16.5% 0.8% 1.6% 1.5% 150.6% 43.0% 3.27 -2.0%
2011-2012 0.3% 1.2% 32.6% 6.2% 1.2% 41.5% 12.4% 0.3% 31.2% 20.3% 2.7% 26.4% 363.0% 52.0% 6.78 -4.1%
2012-2013 3.2% 0.5% 21.0% 2.8% 1.7% 28.3% 14.9% 1.8% 22.4% 16.9% 1.5% 24.9% 250.6% 37.2% 6.47 -1.8%
2013-2014 18.1% 0.0% 22.5% 7.5% 2.3% 21.4% 5.8% 1.4% 35.8% 5.4% 3.9% 20.0% 262.1% 38.9% 6.48 -1.0%
2014-2015 14.8% 1.1% 21.0% 3.7% 1.9% 17.5% 1.4% -0.1% 26.4% 10.0% 2.2% 13.4% 179.5% 31.3% 5.41 -2.0%
2015-2016 12.9% 1.2% 7.2% 1.8% 0.6% 7.0% 16.2% 1.4% 21.3% 9.1% 5.3% 26.1% 172.9% 29.1% 5.60 -1.8%
2016-2017 13.7% 0.7% 13.8% 9.9% 2.4% 12.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 158.7% 47.1% 3.16 -1.1%
Strategy Description
The high-frequency-trading ESA (Earnings
Sentiments Algorithm) is an ultra-low
latency, news-driven long/short strategy
that trades sentiments on company
earnings releases. ESA uses proprietary
evolutionary optimization and machine-
learning mathematics to self-adapt to the
changing financial-economic environment
so that eligible investors can experience
consistently strong capital returns on
investment.
Highlights
Fully automated algorithm
Intraday portfolio turnover
Fee Structure:
2016-2017
2017-2018
2018-2019
2019-onwards
Performance Fee 10% 15% 20% 20%
Management Fee 1% 2% 2% 2%
Operations Portfolio Instrument Selection Criteria
Python or C based coding and collocated
trade executions form the basic
requirements for high-frequency algorithm
automation in terms of account
management, trade signals & watchlist,
and model & execution optimization.
Operational costs are estimated at
USD$260,000 annually.
Established and large stock exchanges (e.g. NYSE, NASDAQ)
Instrument’s 52-week average daily trading volume must equal or
exceed 3 million shares
Minimum market capitalization of USD$5 billion
Watchlist scalable to est. 201 instruments in NYSE & NASDAQ
Key Criteria: Correlation profile minimum of 75%
Top 10 traded equities
Carmax Inc
Southern Co
Interpublic Group of Companies Inc
Annaly Capital Management Inc
Norfolk Southern Corp
Cooper Companies Inc
General Mills Inc
M&T Bank Corp
W W Grainger Inc
Harley-Davidson Inc
Equity Style
Market Cap
Large
Medium
Small
Value Blend Growth
Growth Strategy
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Strategy Chart Descriptions
Highest Gross Profit: 119.85% Lowest Gross Loss: -1.47% Average Gross Profit: 13.24% Average Gross Loss -0.22%
Profitable
Trades Losing Trades
Jan 136 52 Feb 145 38 Mar 32 8 Apr 199 41 May 101 24 Jun 38 5 Jul 217 55
Aug 105 20 Sep 27 6 Oct 199 46 Nov 91 24 Dec 36 7
Profit Loss Win Ratio
2007 63 14 82% 2008 137 28 83% 2009 176 32 85% 2010 130 50 72% 2011 103 29 78% 2012 156 34 82% 2013 160 35 82% 2014 154 46 77% 2015 134 36 79% 2016 106 20 84%
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Other Algorithms in R&D
Bollinger Band Algorithm
An evolutionary algorithm that varies its moving averages and threshold parameter for a contrarian Bollinger-Band
sentiment-scoring trading algorithm. This will be a high latency strategy which trade daily upwards to weekly or
monthly depending on the optimized number of days to hold for a particular instrument for maximum risk-
adjusted returns.
Technical multi-indicator Momentum Algorithm
A machine learning algorithm that uses multiple technical momentum-based indicators as the basis for its multi-
factor model. This will be a high latency strategy which trade daily upwards to weekly or monthly depending on
the optimized number of days to hold for a particular instrument for maximum risk-adjusted returns.
More to come…
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Risk Management
Portfolio Risk
Operational Risk
Structural Risk
− Algorithm diversification for long-term sustainable Sortino Ratio − Weight allocation of algorithms based on individual algorithm Sortino ratio − Genetic algorithms self-adapt to varying market conditions − Capital diversified across multiple securities (and multiple algorithms in the long run) − Initial position size shall never exceed 2% of the entire managed capital − Execution algorithms are used to minimize market impact, optimize entry/exit points and
allocate capital across instruments.
If the algorithm: − Loses more than 15% of managed capital over consecutive trades, or − The monthly win-rate drops below 50%,
Whichever, comes first, the investment manager reserves the right to take the algorithm offline for further analysis
− Enterprise-grade virtual machines to allow trading and risk management to operate efficiently
Trailing stop-loss programmed into algorithm – value determined by optimization maximization of Sortino ratio
Long/Short output must be above threshold, which is based on margin of error, in order to generate trading signals
Algorithm includes macro-economic data to account for sudden economic volatility
If the algorithm’s monthly win-rate drops below 65%, trading stops until theoretical win-rate recovers at or above
65% (which can happen for a maximum of a few days only)
Reserve ratio, i.e. the maximum % of managed capital in one instrument. is optimized by the algorithm, shall be
limited to a maximum of 25%
Trading Risk
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Operations
Execution optimized algorithmically to reduce market
impact and slippage
Automated and Quantitative trading style
Trading takes place from 07:00 EST to 18:00 EST daily
Quantopian or Numerai or LightSpeed Trading Platform or
Prime Brokerage (refer to Executive Summary for details)
(We manage the programming and algorithms, You invest only)
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Trading Process
1. Download of Historical Data before trading period
2. Pre-Optimization of Algorithm parameters before trading
period to reduce trading latency
3. Download of Real-Time auto-parsed Data into algorithm at
time of data release
4. Trade Signal produced as either Long or Short signal
5. Trade Execution via Algorithmic Broker with their
proprietary execution algorithms using our execution logic
6. Max 5-minute holding period as timed execution, then
reverse signal to exit the traded position
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Scalability
Scaling of all of EquitySoft’s trading algorithms, including the
Earnings Sentiments Algorithm, across multiple venues can
increase the amount of tradable capital under EquitySoft over time.
Here are the means of scalability of the ESA’s trading capacity:
1. Stock Exchanges
NYSE & NASDAQ
□ Australian SE
□ London SE Group
□ Deutsche Börse
□ TMX Group
□ SIX Swiss Exchange
□ Euronext
□ Hong Kong Exchanges
□ Korea Exchange
2. Asset Classes
Equities
□ Options
□ Futures
3. Equities Coverage
< 300 equities
□ < 600 equities
□ < 1000 equities
□ > 2000 equities
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Investment Terms
Investment Partnership: Managed Account
Fee Structure
AUM Target USD$500,000 USD$2,000,000 USD$10,000,000
Incentive Allocation 10% 15% 20%
Management Fee 1% 2% 2%
Minimum Investment per Investor USD$10,000 USD$100,000 USD$150,000
Operational Funding Target USD$20,000 USD$260,000 USD$260,000
Subscriptions Monthly
High-Water Mark Yes
Lock-Up Minimum 1 year
(below 70% win-rate trigger or 15% drawdown trigger)
Redemptions Quarterly
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Contact Information
Sean Chan
Chairman, CEO and Founder of EquitySoft
Investments Valuations Inc.
Vancouver BC V6S 1E5
Canada: +1 (604) 715-6298
Singapore: +65 9150-5743
Email: [email protected]
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Legal Considerations:
This document serves to provide information about EquitySoft’s Earnings Sentiments
Algorithm trading fund only.
Acceptance of or receiving of this document not act as a part of a contract of or
confirmation of eligibility to invest as this is subject to the approval of the relevant
authority or certified securities lawyer by the respective and relevant jurisdiction.
Investment into EquitySoft Investments is subject to approval by the proper legal
authority or a certified securities lawyer from the relevant jurisdiction(s) and, as such,
legal and proper consultation from a proper and/or certified authority is required. Hence,
investment capital can only be sourced from legally eligible investors and is therefore not
open to the general public of accredited investors.
Eligible Investors must fall within the Exempted Category of Investors under the laws
and regulations of the SEC.
All trades are subject to the discretion of the trading algorithm and the investor accepts
risks associated with algorithm trading in general and with such an investment/trading
methodology.
All information within this document is for reference only and may not completely reflect
the full nature of the ESA algorithm. An updated version will always better reflect the
data of the ESA algorithm. For additional information and/or clarification, please kindly
contact the Chairman of EquitySoft Investments Valuations Inc.
