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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. 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? 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. A higher frequency strategy will require greater sampling rate of standard deviation, but a shorter overall time period of measurement, for instance.

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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?

Trading strategy: TrendPlus

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.

50-Pips a Day Forex Strategy

One can have a very profitable strategy, even if the number of losing trades exceed the number of winning trades. Momentum strategies tend to have this pattern as they rely on a small number of "big hits" in order to be profitable. Mean-reversion strategies tend to have opposing profiles where more of the trades are "winners", but the losing trades can be quite severe.

Maximum Drawdown - The maximum drawdown is the largest overall peak-to-trough percentage drop on the equity curve of the strategy. Momentum strategies are well known to suffer from periods of extended drawdowns due to a string of many incremental losing trades. Many traders will give up in periods of extended drawdown, even if historical testing has suggested this is "business as usual" for the strategy.

You will need to determine what percentage of drawdown and over what time period you can accept before you cease trading your strategy. This is a highly personal decision and thus must be considered carefully. Capacity determines the scalability of the strategy to further capital. Many of the larger hedge funds suffer from significant capacity problems as their strategies increase in capital allocation. Parameters - Certain strategies especially those found in the machine learning community require a large quantity of parameters.

Every extra parameter that a strategy requires leaves it more vulnerable to optimisation bias also known as "curve-fitting". You should try and target strategies with as few parameters as possible or make sure you have sufficient quantities of data with which to test your strategies on. Benchmark - Nearly all strategies unless characterised as "absolute return" are measured against some performance benchmark. The benchmark is usually an index that characterises a large sample of the underlying asset class that the strategy trades in. You will hear the terms "alpha" and "beta", applied to strategies of this type.

We will discuss these coefficients in depth in later articles. Obtaining Historical Data Nowadays, the breadth of the technical requirements across asset classes for historical data storage is substantial. Let's begin by discussing the types of data available and the key issues we will need to think about: Fundamental Data - This includes data about macroeconomic trends, such as interest rates, inflation figures, corporate actions dividends, stock-splits , SEC filings, corporate accounts, earnings figures, crop reports, meteorological data etc.

This data is often used to value companies or other assets on a fundamental basis, i. It does not include stock price series. Some fundamental data is freely available from government websites. Other long-term historical fundamental data can be extremely expensive. Storage requirements are often not particularly large, unless thousands of companies are being studied at once.

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News Data - News data is often qualitative in nature. It consists of articles, blog posts, microblog posts "tweets" and editorial.

Machine learning techniques such as classifiers are often used to interpret sentiment. This data is also often freely available or cheap, via subscription to media outlets. The newer "NoSQL" document storage databases are designed to store this type of unstructured, qualitative data.

Asset Price Data - This is the traditional data domain of the quant. It consists of time series of asset prices.

Equities stocks , fixed income products bonds , commodities and foreign exchange prices all sit within this class. Daily historical data is often straightforward to obtain for the simpler asset classes, such as equities. Thank you for your hard work in compiling this list. Very informative! These are among my all time favorites, as i follow their strategies.