The document discusses the feasibility of creating custom quantitative trading strategies, outlining five basic strategies: mean reversion, momentum, valuation, sentiment, and seasonality. Each strategy is examined in terms of intuition, reproducibility, data access, capacity, and common pitfalls, emphasizing the importance of understanding their underlying principles. Additionally, it highlights the popularity of these strategies among retail quants using Quantopian, with insights into top shared algorithms.

2. DIY Quant strategies:
Is it possible to roll your own?
Jess Stauth, PhD
VP Quant Strategy
Bay Area Algorithmic Trading Meetup
Hacker Dojo * February 6, 2014

3. What makes a good equity quant strategy?
 Intuition. If you can‟t explain why it works, it doesn‟t
work.
 Reproducibility. If you can‟t backtest it, it doesn‟t work
(note the inverse does not necessarily hold).
 Access to data. If you can‟t get the signal (or get it in
time) you can‟t trade it. ($$$)
 Capacity/Execution You can‟t push a camel through the
eye of a needle. (1/$$$)

4. 5 Basic Quant Strategies
1. Mean Reversion – What goes up… (special case: Pairs Trade)
2. Momentum – The trend is your friend.
3. Valuation – Buy low, sell high.
4. Sentiment – Buy the rumor, sell the news.
5. Seasonality – Sell in May and go away.
Out of scope for today‟s talk:
 Acronym soup (e.g. ML, OLMAR, PCA, ICA, OLS, etc.)
 Portfolio construction, risk optimization, etc.
 Asset clases

5. Pairs Trading
 Intuition: Find two assets linked to a single underlying „value‟
and exploit transient mispricing between them.
 Reproducibility: The phenomenon is well documented1,2.
 Data: For basic strategies all you need is pricing.
 Capacity: Can be quite small depending on the instruments.
Common pitfalls:
 Ignore the intuition requirement at your own peril! Cointegration works great, until it doesn‟t.
 Market neutral or „hedged‟ strategy, so you are forgoing any upward drift in the longer term.
1. Pairs Trading, Vidyamurthy 2004
2. Quantitative Trading, Chan 2009

6. Pairs Trading
Simplistic Intuition (cont‟d): If you assume the spread between stock 1 and stock 2 is
„stationary‟ and „normally distributed‟, then statistically you should be able to make money
by „buying‟ or „selling‟ the spread when it takes on extreme tail values.
Zx = (Price Stock1 – Price Stock2)/ Price Stock1

7. Pairs Trading: EWA/EWC Pair
6/06 – 6/12
Huapu Pan (NYC Algo Trading meetup member) Posted 12/19/13
“Ernie Chan‟s EWA/EWC Pair Trading”
https://www.quantopian.com/posts/ernie-chans-ewa-slash-ewc-pair-trading

8. Momentum Trading
 Intuition: Comes in many flavors (stock level, sector level, asset
class level) but comes back to the behavioral bias of „herding‟.
 Reproducibility: The phenomenon is well documented1.
 Data: For basic strategies all you need is pricing.
 Capacity: Can be quite small depending on the instruments.
Common pitfalls:
 The trend is your friend, until it isn‟t. Reversals can be devastating, especially when using
leverage.
1. Jegadeesh and Titman, Returns to Buying Winners and Selling Losers: Implications for Stock
Market Efficiency. Journal of Finance March 1993
2. Faber, A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management 2013

9. Momentum Trading
Simple rules based approach
 Rank 1 > N stocks (sectors) by : [r20 – r200]
 Buy top K stocks (sectors) where absolute
momentum (20 vs. 200 day MA) > some
threshold.
 Else, hold cash.

10. Momentum Trading – Meb Faber RS Strategy
Backtest range: 11/04 – 2/13
John Chia Posted Feb 2013
“Mebane Faber Relative Strength Strategy with MA Rule”
https://www.quantopian.com/posts/mebane-faber-relative-strength-strategy-with-ma-rule

11. Valuation
 Intuition: In a nutshell, bargain shopping. Use fundamental ratio
analysis to identify stocks trading at a discount (or premium) and
buy (or sell) them accordingly.
 Reproducibility: The phenomenon is well documented.
 Data: Requires good coverage (breadth and depth) of
normalized corporate fundamental data.
 Capacity: Small cap stocks can be riskier, and higher friction to
trade.
Common pitfalls:
 Some cheap stocks are cheap for a reason. “Catch a falling knife” adage.

12. Valuation
Simple example: use price to earnings ratio as a proxy for „value‟
where low P/E looks „cheap‟ and high P/E looks „expensive‟.
 Rank universe 1-100 (or sector universe) on P/E
 Long only: buy the bottom (lowest P/E) decile
 Market neutral: buy the bottom decile, sell the top decile
In practice, a quant model would typically blend a number of
backward looking ratios an forward looking estimates along with
making sector specific adjustments and other bells, whistles.

13. Valuation: Screen on corporate fundamentals
Backtest range 11/25/2009 – 10/10/2013
Sam Lunt (11/4/2013) “Using Fetcher with Quandl”
https://www.quantopian.com/posts/using-the-fetcher-with-quandl

14. Sentiment: Short sellers
 Intuition: Follow the (short) money. Short sellers are the „smart
money‟, their trades are $ for $ higher conviction (to balance
risk).
 Reproducibility: The phenomenon is well documented.
 Data: Bi-monthly (delayed) short interest can be scraped from
NASDAQ. Borrow rates, real-time daily short interest data
aggregated from brokers is available for $$$.
 Capacity: Can be quite small depending on the instruments.
Common pitfalls:
 Beware the Short Squeeze! Crowded short trades can lead to a squeeze as short sellers rush to close
positions.

15. Sentiment: Short sellers
 Rank stocks 1 > N on Days To Cover ratio*
 Buy top 10%, short bottom 10%
 Rebalance periodically
*Days to cover =
Shares Held Short
Avg Daily Trade Share volume
The number of days of „average‟ trading it would take to
unwind the existing short positions.

16. Sentiment: Short sellers – Rank on Days to Cover
Backtest range: 3/15/12 – 3/15/13
Fawce (April 2013)
“Ranking and Trading on Days to Cover”
https://www.quantopian.com/posts/ranking-and-trading-on-days-to-cover

17. Seasonality
 Intuition: Sometimes (calendar driven fund flows
e.g. month end).
 Reproducibility: There‟s healthy debate on this
one.
 Data: end of day pricing and a calendar.
 Capacity: Depends on the instruments.
Common pitfalls:
 Overfitting / data mining is rampant in this type of analysis.

18. Seasonality
Simplest example is a simple 100% stock/bond annual
rotation model.
 Buy and hold equities (SPY) October thru April
 Buy and hold bonds (BSV) May thru Sept.

19. Seasonality: Sell in May
Backtest range: 10/1/09 – 12/31/12
Jess(May 2013)
“Sell in May and go away”
https://www.quantopian.com/posts/time-to-sell-in-may-and-go-away

20. Which of these strategies are most popular among
the „retail‟ or individual quants using Quantopian?
 Mean Reversion
 Momentum
 Valuation
 Sentiment
 Seasonality
 Other

21. 25 Top Shared Algorithms of All Time
Combo Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Post Title
Replies
Google Search Terms predict market movements
OLMAR implementation
Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics
Global Minimum Variance Portfolio
discuss the sample algorithm
ML - Stochastic Gradient Descent Using Hinge Loss Function
Mebane Faber Relative Strength Strategy with MA Rule
OLMAR w/ NASDAQ 100 & dollar-volume
Bollinger Bands With Trading
Brent/WTI Spread Fetcher Example
Ernie Chan's Pairs Trade
Ranking and Trading on Days to Cover
Using the CNN Fear & Greed Index as a trading signal
Determining price direction using exponential and log-normal distributions
Time to sell in may and go away?
Simple Mean Reversion Strategy
Neural Network that tests for mean-reversion or momentum trending
Using weather as a trading signal
Momentum Trade
Trading Strategy: Mean-reversion
Global market rotation strategy
trading earnings surprises with Estimize data
Turtle Trading Strategy
SPY & SH algorithm - please review
New Feature: Fetcher!
TOTALS:
Views
Clones
64
64
57
28
12
10
22
31
18
17
15
4
18
9
27
6
4
6
5
13
53
34
11
21
27
31913
26039
15117
10222
18348
20400
11104
7760
8363
10821
10387
24906
9212
9539
8192
11794
10062
11940
8800
8228
7621
7496
7815
7443
7507
809
697
839
700
2882
972
617
697
560
327
328
379
318
606
261
270
402
199
455
213
94
129
299
194
108
576
311,029
13,355

22. 25 Top Shared Algorithms of All Time
Combo Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Post Title
Google Search Terms predict market movements
OLMAR implementation
Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics
Global Minimum Variance Portfolio
discuss the sample algorithm
ML - Stochastic Gradient Descent Using Hinge Loss Function
Mebane Faber Relative Strength Strategy with MA Rule
OLMAR w/ NASDAQ 100 & dollar-volume
Bollinger Bands With Trading
Brent/WTI Spread Fetcher Example
Ernie Chan's Pairs Trade
Ranking and Trading on Days to Cover
Using the CNN Fear & Greed Index as a trading signal
Determining price direction using exponential and log-normal distributions
Time to sell in may and go away?
Simple Mean Reversion Strategy
Neural Network that tests for mean-reversion or momentum trending
Using weather as a trading signal
Momentum Trade
Trading Strategy: Mean-reversion
Global market rotation strategy
trading earnings surprises with Estimize data
Turtle Trading Strategy
SPY & SH algorithm - please review
New Feature: Fetcher!
Replies
Views
Clones
64
64
57
28
12
10
22
31
18
17
15
4
18
9
27
6
4
6
5
13
53
34
11
21
27
31913
26039
15117
10222
18348
20400
11104
7760
8363
10821
10387
24906
9212
9539
8192
11794
10062
11940
8800
8228
7621
7496
7815
7443
7507
809
697
839
700
2882
972
617
697
560
327
328
379
318
606
261
270
402
199
455
213
94
129
299
194
108

23. 25 Top Shared Algorithms of All Time
Categorized
Volatility
5%
Technical
3%
Seasonality
3%
Portfolio Risk
6%
Momentum
18%
Mean
Reversion
37%
Area ~ page views
Sentiment
28%
What‟s missing from this picture??

24. Thank You.
Questions?