Tearsheet feedback webinar 10.10.18

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The document provides feedback on trading algorithms submitted for review. It includes disclaimers about the intended purpose of the feedback and assumptions made. Specific feedback is then provided on 9 algorithms, noting areas like high turnover, concentrated portfolios, and risk exposures. The feedback aims to help improve the algorithms in certain thematic areas.

Technology

Overview – tearsheets
submitted for feedback
Highlights for discussion

Disclaimer
Quantopian provides this presentation to help people write trading
algorithms - it is not intended to provide investment advice. More
specifically, the material is provided for informational purposes only and
does not constitute an offer to sell, a solicitation to buy, or a
recommendation or endorsement for any security or strategy, nor does it
constitute an offer to provide investment advisory or other services by
Quantopian. In addition, the content neither constitutes investment advice
nor offers any opinion with respect to the suitability of any security or any
specific investment. Quantopian makes no guarantees as to accuracy or
completeness of the views expressed in the website. The views are subject to
change, and may have become unreliable for various reasons, including
changes in market conditions or economic circumstances.

Disclaimers
• Feedback is based solely on the data in the notebooks shared on
forums thread (e.g. if you provided a 3y backtest that’s what I’ll give
feedback on)
• I’m assuming something like $10m capital base & 5bps fixed cost
model, though I don’t see those directly in the tearsheets
• I won’t go into detailed feedback on the economic rationales
provided, but bravo to those who provided them!
• Feedback is based on my own (Q’s) perspective and the style and
characteristic preferences & limitations the come with that
perspective.

Algo 2 – long st reversal, short size and
sometimes vol

Algo 3 – long st reversal, short size and vol

Algo 4 – long mo, short st reversal sometimes
vol

Algo 4 – long tech, short consumer cyclical

Algo 7 – on rebalance days, t/o is v high

Algo 8 – v tightly controlled risk exposures
(see scale)

Some thematic areas for improvement:
- concentrated portfolios
- high turnover
Avg Pos
Count
Avg daily t/o Peak t/o
1 150-200 13% 100%
2 32 15% 15%
3 32 15% 15%
4 24 18% 40%
5 225 18% 18%
6 75 64% 100%
7 40 10% 100%
8 400 15% 20%
9 1000 7% 20%

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Factor modeling and style premia are historically well documented and extensively researched in generating abnormal returns. Despite the large amount of research around factors, there is less clarity around effectively capturing and extracting this alpha from a given universe. In this presentation, Cheng will demonstrate different techniques for combining multiple factors, and the rationale behind maximizing alpha while maintaining scalability.

"How to Run a Quantitative Trading Business in China with Python" by Xiaoyou ...

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From QuantCon 2017: Running a quantitative trading business in China used to be very difficult and require strong IT skills, however it's getting much easier nowadays, when traders with no professional IT training can also do all the tasks in quantitative trading using Python. In this sharing session, Xiaoyou will share his experience in using Python for data collection, strategy development and automated trading. He will also introduce some related open source projects including TuShare, quantOS, vn.py and so on.

"Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Boo...

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From QuantCon 2017: Financial trading is essentially a search problem. The buy-side agent needs to find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price. Ilija will present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs. The talk will continue with the challenges of applying reinforcement learning to optimal trading and their potential solutions. Finally, Ilija will share the system architecture and discuss future work.

"Building Diversified Portfolios that Outperform Out-of-Sample" by Dr. Marcos...

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Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods. Read the corresponding white paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2713516

"From Insufficient Economic data to Economic Big Data – How Trade Data is red...

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Over the last 10 years, the world of economics has been playing a catching up game and many economists have been struggling to explain their theories. The world has adopted technology in nearly every aspect of life, from phones to cars; however, good, reliable and quality data in economics is still elusive. There is over reliance on macroeconomic principles in comparison to the quality of data available. Macro-economic figures move markets, only to get revised one, two or three times in the following months. Some fields of economic study are exceptions, such as analysing trade data. Trade data, with the support of technology, has become readily available and can now be analysed in depth, providing actual numbers indicating the health and state of economies. Trade data, which is export and import information of all the goods and services from one country to another, can be seen as an inseparable marker of real economic activity. It can be used to predict various market indicators exhibiting high correlations, from currencies to commodities to equities to macroeconomic data, with varying degree of certainties. Trade data, at an in-depth level, acts like a compilation of millions of real life mathematical functions. This presentation explores this new economic area of trade data as a quantitative tool, its intense big data analysis and its applications in trading markets.

"Machine Learning Approaches to Regime-aware Portfolio Management" by Michael...

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"Don't Lose Your Shirt Trading Mean-Reversion" by Edith Mandel, Principal at ...

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"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...

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Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities. Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods. He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.

"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to Portfolio ...

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Maxwell will present the methodologies and results behind the algorithm that has been developed by 1QBit, named Quantum Hierarchical Risk Parity, or QHRP. This is an extension of the work done by Marcos Lopez de Prado on Hierarchical Risk Parity in his paper "Building Diversified Portfolios that Outperform Out-of-Sample." QHRP tackles the problem of minimizing the risk of a portfolio of assets using a quantum-inspired approach. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to demonstrate strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data. The quantum-ready approach to portfolio optimization is based on an optimization problem that can be solved using a quantum annealer. The algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. The results of real market data used to benchmark this approach against other common portfolio optimization methods will be shared in this presentation. View the White Paper: https://bit.ly/2k5xTxW.

"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...

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Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy. Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.

"On the Bayesian Interpretation of Black–Litterman" by Dr. Gordon Ritter, Sen...

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We will present the most general model of the type considered by Black and Litterman (1991) after fully clarifying the duality between Black–Litterman optimization and Bayesian regression. Our generalization is itself a special case of a Bayesian network or graphical model. As an example, we will work out in full detail the treatment of views on factor risk premia in the context of APT. We will also consider a more speculative example in which the portfolio manager specifies a view on realized volatility by trading a variance swap.

"Correlated Volatility Shocks" by Dr. Xiao Qiao, Researcher at SummerHaven In...

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Commonality in idiosyncratic volatility cannot be completely explained by time-varying volatility. After removing the effects of time-varying volatility, idiosyncratic volatility innovations are still positively correlated. This result suggests correlated volatility shocks contribute to the comovement in idiosyncratic volatility. Motivated by this fact, we propose the Dynamic Factor Correlation (DFC) model, which fits the data well and captures the cross-sectional correlations in idiosyncratic volatility innovations. We decompose the common factor in idiosyncratic volatility (CIV) of Herskovic et al. (2016) into the volatility innovation factor (VIN) and time-varying volatility factor (TVV). Whereas VIN is associated with strong variation in average returns, TVV is only weakly priced in the cross section A strategy that takes a long position in the portfolio with the lowest VIN and TVV betas, and a short position in the portfolio with the highest VIN and TVV betas earns average returns of 8.0% per year.

"Identifying Credibility of News" by Dr. Sameena Shah, Director of Research f...

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