Options research trading strategies module mock test
This module is NOT designed to produce a quantity, that is carried out via the position-sizing module. This is because I think it is far easier to increase expected returns by reducing costs through proper risk management and position sizing, rather than chasing strategies with "more alpha".
It is the area which requires the most development time and quality assurance testing. The goal of this system is to go from the current portfolio to the desired portfolio , while minimising risk and reducing transaction costs. The module ties together the strategy, risk, position sizing and order execution capabilities of the sytem. It also handles the position calculations while backtesting to mimic a brokerage's own calculations.
The primary advantage of using such a complex system is that it allows a variety of financial instruments to be handled under a single portfolio. This is necessary for insitutional-style portfolios with hedging. Such complexity is very tricky to code in a For-Loop backtesting system. Separating out the risk management into its own module can be extremely advantageous. The module can modify, add or veto orders that are sent from the portfolio. In particular, the risk module can add hedges to maintain market neutrality.
It can reduce order sizes due to sector exposure or ADV limits. It can completely veto a trade if the spread is too wide, or fees are too large relative to the trade size.
A separate position sizing module can implement volatility estimation and position sizing rules such as Kelly leverage. In fact, utilising a modular approach allows extensive customisation here, without affecting any of the strategy or execution code.
Such topics are not well-represented in the quant blogosphere. However, this is probably the biggest difference between how institutions and some retail traders think about their trading. Perhaps the simplest way to get better returns is to begin implementing risk management and position sizing in this manner.
We must consider transactional issues such as capacity, spread, fees, slippage, market impact and other algorithmic execution concerns, otherwise our backtesting returns are likely to be vastly overstated. The modular approach of an Event-Driven system allows us to easily switch-out the BacktestExecutionHandler with the LiveExecutionHandler and deploy to the remote server. We can also easily add multiple brokerages utilising the OOP concept of "inheritance".
One issue to be aware of is that of "trust" with third party libraries. There are many such modules that make it easy to talk to brokerages, but it is necessary to perform your own testing. Make sure you are completely happy with these libraries before committing extensive capital, otherwise you could lose a lot of money simply due to bugs in these modules.
Retail quants can and should borrow the sophisticated reporting techniques utilised by institutional quants. Consistent incremental improvements should be made to this infrastructure. This can really enchance returns over the long term, simply by eliminating bugs and improving issues such as trade latency. Don't simply become fixated on improving the "world's greatest strategy" WGS. The WGS will eventually erode due to "alpha decay". Others will eventually discover the edge and will arbitrage away the returns.
However, a robust trading infrastructure, a solid strategy research pipeline and continual learning are great ways of avoiding this fate. Infrastructure optimisation may be more "boring" than strategy development but it becomes significantly less boring when your returns are improved! Deployment to a remote server, along with extensive monitoring of this remote system, is absolutely crucial for institutional grade systems. Retail quants can and should utilise these ideas as well.
A robust system must be remotely deployed in "the cloud" or co-located near an exchange. Often things fail right at the worst time and lead to substantial losses. There are many vendors on offer that provide relatively straightforward cloud deployments, including Amazon Web Services, Microsoft Azure, Google and Rackspace. For software engineering tasks vendors include Github, Bitbucket, Travis, Loggly and Splunk, as well as many others.
Unfortunately there is no "quick fix" in quant trading. It involves a lot of hard work and learning in order to be successful. Perhaps a major stumbling block for beginners and some intermediate quants! Such strategies always eventually succumb to alpha decay and thus become unprofitable. Hence it is necessary to be continually researching new strategies to add to a portfolio. In essence, the "strategy pipeline" should always be full.
It is also worth investing a lot of time in your trading infrastructure. Spend time on issues such as deployment and monitoring. Always try and be reducing transaction costs, as profitability is as much about reducing costs as it is about gaining trading revenue. I recommend writing your own backtesting system simply to learn. You can either use it and continually improve it or you can find a vendor and then ask them all of the questions that you have discovered when you built your own.
It will certainly make you aware of the limitations of commercially available systems. Finally, always be reading, learning and improving. There are a wealth of textbooks, trade journals, academic journals, quant blogs, forums and magazines which discuss all aspects of trading.
Perhaps my two biggest takeaways from working in an institutional setting are the vast chasm between backtests and live trading, as well as the importance of thinking at a portfolio level and the associated risk management thereof. Coming from a scientific background means that I'm very passionate about knowledge sharing and open-source software. Regular visitors will know that this is a running theme through QuantStart.
QuantStart itself was founded in late A lot has changed in quantitative finance since then! The site discusses quant trading, quant careers, data science, machine learning and mathematics education. In particular, the site discusses a lot of implementation details that are often ignored, but I find to be absolutely necessary. The material on this website 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 services by Quantopian.
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Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. All investments involve risk — including loss of principal. You should consult with an investment professional before making any investment decisions. Back to all posts. QuantCon Guest Post by Michael Halls-Moore About This Post The post is suitable for those who are beginning quantitative trading as well as those who have had some experience with the area.
What Is A Backtest? A backtest is the application of trading strategy rules to a set of historical pricing data. Backtesting Pitfalls There are many pitfalls associated with backtesting.
Some of the more common pitfalls include: In-Sample Testing - This occurs when you utilise the same data to "train" your trading models as well as to "test" it. It almost always inflates the performance of a strategy beyond that which would be seen in live trading. This is because it has not been validated on unseen data, which will likely differ markedly from the training data. In essence, it is a form of overfitting. By failing to take into account this changing composition over a backtest, trading strategies are automatically "picking the winners" by virtue of ignoring all the companies that fell out of the index due to low market capitalisation.
Hence it is always necessary to use survivorship-bias free data when carrying out longer-term backtests. Look-Ahead Bias - Future data can "sneak in" to backtests in very subtle ways.
Consider calculating a linear regression ratio over a particular time-frame. If this ratio is then used in the same sample, then we have implicitly brought in future data and thus will have likely inflated performance.
Event-driven backtesters largely solve this problem, as we will discuss below. Market Regime Change - This concerns the fact that stock market "parameters" are not stationary.
That is, the underlying process generating stock movements need not have parameters that stay constant in time. This makes it hard to generalise parametrised models of which many trading strategies are instances of and thus performance is likely to be higher in backtests than in live trading.
Transaction Costs - Many For-Loop backtests do not take into account even basic transaction costs, such as fees or commissions. This is particularly true in academic papers where backtests are largely conducted free of transaction costs. Unfortunately it is all too easy to find strategies that are highly profitable without transaction costs, but make substantial losses when subjected to a real market. Typical costs include spread, market impact and slippage. All of these should be accounted for in realistic backtests.
Hence it is unlikely that some of the more extreme values seen including the High and Low price of the day would likely be obtained by a live trading system. Such "order routing" needs to be considered as part of a model. Capacity Constraints - When backtesting it is easy to utilise an "infinite" pot of money. However, in reality capital, as well as margin, is tightly constrained. It is necessary also to think of Average Daily Volume ADV limits, especially for small-cap stocks where it is possible that our trades might indeed move the market.
Such "market impact" effects would need to be taken into account for risk management purposes. Benchmark Choice - Is the choice of benchmark against which the backtested strategy is being measured a good one? Would a basket of other commodity trading funds make more sense? Robustness - By varying the starting time of your strategy within your backtest do the results change dramatically?
It should not matter for a longer term strategy whether the backtest is started on a Monday or a Thursday. However, if it is sensitive to the "initial conditions" how can you reliably predict future performance when live trading? However, overfitting is a broader problem for all supervised machine learning methods. The only real way to "solve" this problem is via careful use of cross-validation techniques. Even then, we should be extremely careful that we haven't simply fitted our trading strategies to noise in the training set.
Psychological Tolerance - Psychology is often ignored in quant finance because supposedly it is removed by creating an algorithmic system. However, it always creeps in because quants have a tendency to "tinker" or "override" the system once deployed live. In addition, what may seem tolerable in a backtest, might be stomach-churning in live trading.
For-Loop Backtest Systems A For-Loop Backtester is the most straightforward type of backtesting system and the variant most often seen in quant blog posts, purely for its simplicity and transparency.
Here is the pseudo-code for such an algorithm: Advantages For-Loop backtesters are straightforward to implement in nearly any programming language and are very fast to execute. Disadvantages The main disadvantage with For-Loop backtesters is that they are quite unrealistic. Tick Events - Signify arrival of new market data Signal Events - Generation of new trading signals Order Events - Orders ready to be sent to market broker Fill Events - Fill information from the market broker When a particular event is identified it is routed to the appropriate module s in the infrastructure, which handles the event and then potentially generates new events which go back to the queue.
The pseudo-code for an Event-Driven backtesting system is as follows: Advantages There are many advantages to using an Event-Driven backtester: There is the possibility of introducing bias indirectly through a pre-researched model, however. Code Re-Use - For live trading it is only necessary to replace the data handler and execution handler modules. This means there are usually far less bugs to fix.
Portfolio Level - With an Event-Driven system it is much more straightforward to think at the portfolio level.
Introducing groups of instruments and strategies is easy, as are hedging instruments. Can introduce leverage and methodologies such as Kelly's Criterion easily. Can also easily include sector exposure warnings, ADV limits, volatility limits and illiquidity warnings.
Disadvantages While the advantages are clear, there are also some strong disadvantages to using such a complex system: Tricky to Code - Building a fully-tested Event-Driven system will likely take weeks or months of full-time work. Compliance officers of any company needs to have adequate knowledge of the legal and regulatory requirements for carrying out the business of that company.
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