Wall Street's culture was born in an age of information scarcity. Hoarding information and keeping secrets were the norm. The world has changed. Today's world is defined by information that wants to be free. The new scarcity is people: people with the talent and drive to wring insight from all of that data.
Quantopian's mission is to attract the world's algorithmic and financial talent. We want to attract today's quants, and we want to attract talent that hasn't yet had the opportunity to be a quant. We want to bring this talent together, provide them with the tools that they require, and help them build a community. First and foremost, our community is rooted in openness and sharing. Members share code, know-how, and data. Quantopian sets the tone by providing open-sourced code, discussing our techniques, and supplying the historical data needed for algorithmic investing.
By educating more people about statistical arbitrage and data mining for finance, we aim to dispense with the secrecy and raise the state of the art. Rather than hoard data, we relentlessly push data to our community. We want to diversify the data that can be mined, and permit our members to explore as much as they like. Our members' success in analyzing and investing will help us draw more data and more members to our community. Every individual's success will also help other Quantopians.
Algorithmic finance originated as chart reading. Chartists would look for certain patterns in price history charts. The patterns were always graced with artfully chosen names like 'head and shoulders,' 'spinning top', or 'morning star'. Chart reading looks a lot like palm reading, and for the skeptics among us the similarities don't end with appearances. Still, chart reading is an attempt to infer the balance of buying and selling appetites in the markets from a stock's history. Viewed that way, chart reading pursues the noble goal of prediction. Charting is so common that certain events can trigger market responses, possibly because so many participants infer the same meaning from a stock's price chart.
Analysis grew more sophisticated as chartists gave way to computer scientists writing algorithms. These algorithms have more scientific sounding names like Moving Averages, Volume Weighted Moving Averages, Bollinger Bands, Relative Strength Indicators, and Pearson's Correlation Coefficient. Building technical analysis algorithms looks a lot like modern statistics, and the optimists among us would say the similarities run deep. Technical analysts take algorithmic approaches to the same concept: inferring future behavior from trailing data. In addition to greater sophistication, technical analysts can also test their algorithms over historic data. Imperfect to be sure, but a giant leap from staring at a chart.
Reasonable people can disagree about the 'correctness' of inferring future events from past behavior. Rather than dwell on that question, we choose to point out a different limitation of both charting and modern technical analysis: both interpret the movement of a single stock in isolation. This limitation is both a blessing and a curse.
On the one hand, there is little room for sophisticated statistics or machine learning when you have just a single time series for both your signal and your prediction target.
On the other, technical analysis can still be intuitive, which makes it easier to get acquainted with the idea of automated trading. Often there is a mental leap for people to make from understanding the interpretation of a price series to issuing orders. Because the signals are easy to understand, technical analysis makes for a good initial learning experience to explore risk and performance evaluation as well as order management: the price going above its 30 day moving average is something you can visualize. So, you can focus your attention on the financial and trading aspects of the problem.
Statistical Arbitrage is the grandchild of chart reading. Like technical analysis it relies on algorithms and statistics, but it departs in one very significant way: 'stat arb' looks for relationships among many stocks. The challenge with stat arb is twofold:
- visualizing the relationships can be quite difficult, since the relationships can have high dimensionality
- the data processing load is quite high - a simple linear regression for all stocks results in 32 million individual regressions. Assuming a 10-day window, that can be 320 million individual calculations. To prepare, backtest, and trade a stat arb strategy required both familiarity with the mechanics of trading, knowledge of statistics, and a strong computer science background.
As stat arb matured, the competition to find stat arb strategies that work became a two part race:
- execute the trades faster
- find new ways to identify relationships within market data
We think the pursuit of faster trades reached diminishing returns when the market hit sub-millisecond trade execution. We think that the resulting high level of liquidity is a good thing, but we agree with Thomas Petterffy that pursuing even faster trades "has absolutely no social value".
Finding new relationships in the market data is possible and more important now than ever. In the summer of 2007, there was a sudden meltdown in quantitative trading firms. Subsequent analysis points to quants crowding into the same arbitrage bets, and an unforeseen fund liquidation driving all the quants to unwind those bets concurrently. We believe finding new relationships should permit investments with lower correlation and lower risks.
A revolution in market understanding happens next. We want Quantopian to enable more quants than all of Wall Street combined. We want quants, new and old, to explore and share new ways to view the market. We want to clear away the obstacles that have so far kept all but a few from doing algorithmic investing by:
- simulating with clean, high-quality market data for free
- easy access to markets through trusted brokers
- providing a robust, flexible open-source backtester to permit evaluation and iteration of algorithms
- supporting a community that fosters the exchange of knowledge, ideas, code solutions, and data sources
The community will find new ways to identify market opportunities. It may take the form of new, non-market data sources, like news feeds or Twitter. It may be new algorithmic techniques. Most likely, it will be something we haven't heard of yet: your idea. The one you keep coming back to. The idea you couldn't test without data. The idea that needs backtesting, and iteration, and encouragement from other quants.
Do you want to unleash your idea? This is your chance. Come hack Wall Street.