A comprehensive list of tools for quantitative traders - QuantPedia
Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. What are some beginner quantitative option trading strategies? Ask Question. Asked 1 year, 11 months ago. Active 1 year, 6 months ago. Viewed 3k times. Improve this question. Vishnu Talanki Vishnu Talanki 81 1 1 silver badge 3 3 bronze badges. You could investigate how the hedging error varies with re-hedging frequency, with the assumed volatility, etc.
Add a comment. Active Oldest Votes. You can try for example: Active Collar strategy Calendar Option Strategies Dispersion trading Try to google more, or look for strategies on ssrn.
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Improve this answer. Sign up or log in Sign up using Google. Sign up using Facebook. Nowadays, the breadth of the technical requirements across asset classes for historical data storage is substantial. In order to remain competitive, both the buy-side funds and sell-side investment banks invest heavily in their technical infrastructure. It is imperative to consider its importance.
In particular, we are interested in timeliness, accuracy and storage requirements. I will now outline the basics of obtaining historical data and how to store it. Unfortunately this is a very deep and technical topic, so I won't be able to say everything in this article. However, I will be writing a lot more about this in the future as my prior industry experience in the financial industry was chiefly concerned with financial data acquisition, storage and access.
In the previous section we had set up a strategy pipeline that allowed us to reject certain strategies based on our own personal rejection criteria. In this section we will filter more strategies based on our own preferences for obtaining historical data.
The chief considerations especially at retail practitioner level are the costs of the data, the storage requirements and your level of technical expertise. We also need to discuss the different types of available data and the different considerations that each type of data will impose on us.
Let's begin by discussing the types of data available and the key issues we will need to think about:. As can be seen, once a strategy has been identified via the pipeline it will be necessary to evaluate the availability, costs, complexity and implementation details of a particular set of historical data. You may find it is necessary to reject a strategy based solely on historical data considerations.
This is a big area and teams of PhDs work at large funds making sure pricing is accurate and timely. Do not underestimate the difficulties of creating a robust data centre for your backtesting purposes! I do want to say, however, that many backtesting platforms can provide this data for you automatically - at a cost. Thus it will take much of the implementation pain away from you, and you can concentrate purely on strategy implementation and optimisation.
Tools like TradeStation possess this capability. However, my personal view is to implement as much as possible internally and avoid outsourcing parts of the stack to software vendors. I prefer higher frequency strategies due to their more attractive Sharpe ratios, but they are often tightly coupled to the technology stack, where advanced optimisation is critical.
Now that we have discussed the issues surrounding historical data it is time to begin implementing our strategies in a backtesting engine. This will be the subject of other articles, as it is an equally large area of discussion! Join the QSAlpha research platform that helps fill your strategy research pipeline, diversifies your portfolio and improves your risk-adjusted returns for increased profitability.
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Join the Quantcademy membership portal that caters to the rapidly-growing retail quant trader community and learn how to increase your strategy profitability. How to find new trading strategy ideas and objectively assess them for your portfolio using a Python-based backtesting engine. How to implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with R and Python. Identifying Your Own Personal Preferences for Trading In order to be a successful trader - either discretionally or algorithmically - it is necessary to ask yourself some honest questions.
Sourcing Algorithmic Trading Ideas Despite common perceptions to the contrary, it is actually quite straightforward to locate profitable trading strategies in the public domain. Here is a list of the more popular pre-print servers and financial journals that you can source ideas from: arXiv SSRN Journal of Investment Strategies Journal of Computational Finance Mathematical Finance What about forming your own quantitative strategies? This generally requires but is not limited to expertise in one or more of the following categories: Market microstructure - For higher frequency strategies in particular, one can make use of market microstructure , i.
Different markets will have various technology limitations, regulations, market participants and constraints that are all open to exploitation via specific strategies. This is a very sophisticated area and retail practitioners will find it hard to be competitive in this space, particularly as the competition includes large, well-capitalised quantitative hedge funds with strong technological capabilities.
Fund structure - Pooled investment funds, such as pension funds, private investment partnerships hedge funds , commodity trading advisors and mutual funds are constrained both by heavy regulation and their large capital reserves. Thus certain consistent behaviours can be exploited with those who are more nimble. For instance, large funds are subject to capacity constraints due to their size.
Thus if they need to rapidly offload sell a quantity of securities, they will have to stagger it in order to avoid "moving the market". Sophisticated algorithms can take advantage of this, and other idiosyncrasies, in a general process known as fund structure arbitrage. Classifiers such as Naive-Bayes, et al. If you have a background in this area you may have some insight into how particular algorithms might be applied to certain markets. Evaluating Trading Strategies The first, and arguably most obvious consideration is whether you actually understand the strategy.
Here is the list of criteria that I judge a potential new strategy by: Methodology - Is the strategy momentum based, mean-reverting, market-neutral, directional?
Pair has 97% correlation over last one year, a trade lot ratio of 1 and price ratio of 0.48.
Does the strategy rely on sophisticated or complex! Do these techniques introduce a significant quantity of parameters, which might lead to optimisation bias? Is the strategy likely to withstand a regime change i.
It quantifies how much return you can achieve for the level of volatility endured by the equity curve. Naturally, we need to determine the period and frequency that these returns and volatility i.
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A higher frequency strategy will require greater sampling rate of standard deviation, but a shorter overall time period of measurement, for instance. Leverage - Does the strategy require significant leverage in order to be profitable? Does the strategy necessitate the use of leveraged derivatives contracts futures, options, swaps in order to make a return? These leveraged contracts can have heavy volatility characterises and thus can easily lead to margin calls.
Do you have the trading capital and the temperament for such volatility? Frequency - The frequency of the strategy is intimately linked to your technology stack and thus technological expertise , the Sharpe ratio and overall level of transaction costs. All other issues considered, higher frequency strategies require more capital, are more sophisticated and harder to implement.
However, assuming your backtesting engine is sophisticated and bug-free, they will often have far higher Sharpe ratios. Volatility - Volatility is related strongly to the "risk" of the strategy. The Sharpe ratio characterises this. Higher volatility of the underlying asset classes, if unhedged, often leads to higher volatility in the equity curve and thus smaller Sharpe ratios. I am of course assuming that the positive volatility is approximately equal to the negative volatility.
Some strategies may have greater downside volatility. You need to be aware of these attributes.