Welcome to QuantCon 2015 by John “Fawce” Fawcett, Founder and CEO of Quantopian

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Hello, Welcome to QuantCon 2015! It has been an exciting year at Quantopian, and hosting the first annual QuantCon event here in Manhattan, is a great way to kick off 2015. When I founded Quantopian, my goal was to build a platform that brings together quants, hackers, data scientists, finance professionals, and students all in one vibrant online community. Today, that community has grown to over 34,000 people and continues to grow faster than ever. I couldn’t be more excited to bring over 200 of those community members together today in person! Our schedule today features talks and workshops from a wide variety of experts in the fields of computer science, machine learning, algorithmic trading, market structure, and new economy data. I am a huge fan of every speaker in this lineup and encourage you to take advantage of the diversity of their expertise and to think about new ways to approach your investment strategy goals. Thank you for joining us today and being a part of a truly unique movement in the financial investment space. I also want to thank all of our speakers, sponsors, and staff for their generous contributions to make this day a success. I look forward to hearing from you during and after the event about your experiences. I am already thinking about how we can top this meeting next year! Thank you, John “Fawce” Fawcett This presentation was part of the QuantCon 2015 Conference hosted by Quantopian. Visit us at: www.quantopian.com.

Economy & Finance

Diversification is the only free lunch.
-Markowitz

15 uncorrelated return streams.
The Holy Grail of Investing.
-Ray Dalio
Ref: http://www.scribd.com/doc/134432415/Ray-Dalio-Whats-Going-on-in-the-World-Presentation-pdf#scribd

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The Holy Grail

Quantopian’s Mission
Discover, equip, and reward independent quants.

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Crowd-sourced Alpha: The Search for the Holy Grail of Investing

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Quantopian

This document discusses volatility prediction and trading strategies related to volatility. It begins with an introduction to the author's background and credentials. It then discusses using GARCH models to predict realized volatility and the limitations of using this to predict changes in implied volatility. The document notes that while realized and implied volatility often diverge, the difference between predicted realized volatility and current implied volatility can be traded profitably. It also discusses issues with trading volatility indices like VIX via proxies like VXX compared to the underlying volatility components. Specifically, proxies are subject to impacts of options time decay and rolling that do not affect the actual volatility index.

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Beyond Semantic Analysis Utilizing Social Finance Data Sets to Improve Quanti...

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Dr. Vesna Straser will discuss the differences in expected slippage between live trading, simulation trading and backtesting. Typically in backtesting signal generation and order fill assumptions are simplified to obtain strategy performance data faster. For example, many commercial back testing software providers will work with sampled data such as minute open or close price points and assume that the signal is triggered at the close of one bar and filled at the close price of the next bar, per the assumed slippage model. Simulation trading, however, will typically run on tick trading data (live or replayed) potentially resulting in quite different dynamics versus back testing. Orders are filled per fill assumptions that may vary significantly between different providers. In live trading, orders are triggered and executed immediately under real market conditions and order type. Depending on the trading strategy, live trading results can differ dramatically from back-testing and/or simulation trading. Vesna will outline the issues, analytics to track, factors to consider and how to account for them to achieve “realistic” back-testing results.

A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016

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The document provides an overview of a guided tour of machine learning techniques for traders given by Tucker Balch, a professor of interactive computing at Georgia Tech. It discusses decision trees, k-nearest neighbors, and reinforcement learning algorithms. It also highlights an example trading strategy that uses decision trees to classify stock sentiment data and technical factors to generate long and short signals. Backtests show the combined strategy achieved annual returns over 30% with lower maximum drawdowns than the market.

Statistics - The Missing Link Between Technical Analysis and Algorithmic Trad...

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Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.

The Sustainable Active Investing Framework: Simple, but Not Easy by Wesley Gr...

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To some, the debate of passive versus active investing is akin to Eagles vs. Cowboys or Coke vs. Pepsi. In short, once our preference for one style over the other is established is can become so overwhelming that it becomes a proven fact or incontrovertible reality in our minds. We cannot overemphasize that alpha in the market is no cakewalk. More importantly, being smart, having superior stockpicking skills, or amassing an army of PhDs to crunch data is only half of the equation. Even with those tools, you are still only one shark in a tank filled with other sharks. All sharks are smart, all sharks have a MBA or PhD from a fancy school, and all the sharks know how to analyze a company. Maintaining an edge in these shark infested waters is no small feat, and one that only a handful (e.g., we can count them in one hand) of investors has successfully accomplished. In order too achieve sustainable success as an active investing, one needs both skill and an understanding of human psychology and market incentives (behavioral finance). We start our journey where mine began: as an aspiring PhD student studying under Eugene Fama at the University of Chicago. Let the adventure begin...

Dual Momentum Investing by Gary Antonacci QuantCon 2016

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Gary will begin by reviewing the most common investment vehicles throughout history while explaining their advantages and disadvantages. He will then show how momentum can help accentuate the positives and eliminate the negatives. Using easily understood examples and historical research findings, Gary will show how relative strength momentum can enhance investment return, while trend-following absolute momentum can dramatically decrease bear market exposure. Finally, Gary will show how you can implement and easily maintain your very own dual momentum portfolio using the best assets classes.

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Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

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Return predictability has been a controversial topic in finance for a long time. We show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a simulation which takes positions in SPY proportional to the model forecast. The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. This application of big data ideas to return predictability serves to shift the sentiment associated with market timing.

Being open (source) in the traditionally secretive field of quant finance.

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This document summarizes Jess Stauth's presentation on being open source in quantitative finance. Some key points: - Open source can benefit quant finance through transparency, robustness from many eyes on code, and access to global talent. However, there are also costs and risks around IP leakage. - Many quant firms use a mosaic of open source and proprietary components. Research tools are often open source while backtesting and production systems rely on proprietary models and data. - Quantopian actively maintains several open source projects and contributes to others like Jupyter, Pandas, and PyMC3 to benefit the quant community. Being open can also help hiring.

Stauth common pitfalls_stock_market_modeling_pqtc_fall2018

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Data Modeling the Stock Market Today - Common Pitfalls to Avoid The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be deceptively tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.

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