Strategies de trading

Stratégies de trading de volatilité

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Here are some of the most common active trading strategies that you can use when trading CFDs with Plus Use them, together with our Stock Trading Tips , to craft your own strategies. The strategies you create must be based on your own preferences, your available risk capital, risk tolerance, time you can dedicate to trading and your trading experience. Day trading implies opening and closing a position on the same trading day.

It relies on short-term market movements. It requires in-depth knowledge of the stock being traded and the general market it belongs to. Basically, any event in a particular company can cause volatility, and day traders aim to profit from it. In addition, day trading requires knowledge of the general markets and what makes them move, as well as a good understanding of the general sentiments that drive the market.

Moreover, since day trading focuses on quick moves, it is crucial to be constantly available to act quickly. Keep in mind that day trading requires focus and a lot of work. It is imperative to understand the market, the stock you want to trade and any chart or analysis tool being used. If you plan on day trading, make sure you have access to several reputable news sources, and are well-versed in the different indicators, charts and drawing tools available to you. Position trading is based on identifying trends in order to benefit from the changing directions of the market.

This type of trading concentrates on overall price moves. Traders typically enter a trade once the trend is settled and leave once the trend changes. This may mean holding a position for a couple of days or a few months.

Position traders usually place fewer and bigger trades, looking for substantial gains over longer periods of time. The key is to find the predictable patterns and trends and the stocks that have them. Swing trading is a strategy in which you hold the stocks for a relatively short period of time, until there are price changes swings. It uses technical analysis tools, such as charts and indicators, to identify patterns and trends.

Identifying the price swings allows you to know when to enter and exit your trades in order to catch and ride the wave before it crashes. Swing trading usually aims at smaller profit margins per trade, that can add up to larger sums. Choosing when to enter and when to leave a trade are key parts of any trading strategy, and will play an important role in affecting the results of the trade. In order to know when to enter a trade, it is important to analyse the market and understand what moves the various stocks in the short and long term.

See What Defines a Share Price for more information about the factors that affect share prices. This knowledge will help you identify the most suitable stocks for you to trade and the strategy to build around them. Part of your analysis may be using indicators which help you understand volume, price swings and trends and choose when to enter a trade. With Plus, if your desired entry point is not the current price, you can set price alerts to let you know when the stock reaches a particular rate, or set a future pending order for the system to open a position automatically at your desired price.

It can also be unclear whether the trading strategy is to be carried out with market orders, limit orders or whether it contains stop losses etc. Thus it is absolutely essential to replicate the strategy yourself as best you can, backtest it and add in realistic transaction costs that include as many aspects of the asset classes that you wish to trade in. Here is a list of the more popular pre-print servers and financial journals that you can source ideas from:.

What about forming your own quantitative strategies? This generally requires but is not limited to expertise in one or more of the following categories:. There are, of course, many other areas for quants to investigate. We'll discuss how to come up with custom strategies in detail in a later article. By continuing to monitor these sources on a weekly, or even daily, basis you are setting yourself up to receive a consistent list of strategies from a diverse range of sources.

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The next step is to determine how to reject a large subset of these strategies in order to minimise wasting your time and backtesting resources on strategies that are likely to be unprofitable. The first, and arguably most obvious consideration is whether you actually understand the strategy.

Would you be able to explain the strategy concisely or does it require a string of caveats and endless parameter lists? In addition, does the strategy have a good, solid basis in reality? For instance, could you point to some behavioural rationale or fund structure constraint that might be causing the pattern s you are attempting to exploit? Would this constraint hold up to a regime change, such as a dramatic regulatory environment disruption? Does the strategy rely on complex statistical or mathematical rules? Does it apply to any financial time series or is it specific to the asset class that it is claimed to be profitable on?

You should constantly be thinking about these factors when evaluating new trading methods, otherwise you may waste a significant amount of time attempting to backtest and optimise unprofitable strategies. Once you have determined that you understand the basic principles of the strategy you need to decide whether it fits with your aforementioned personality profile. This is not as vague a consideration as it sounds!

Strategies will differ substantially in their performance characteristics. There are certain personality types that can handle more significant periods of drawdown, or are willing to accept greater risk for larger return. Despite the fact that we, as quants, try and eliminate as much cognitive bias as possible and should be able to evaluate a strategy dispassionately, biases will always creep in.

Thus we need a consistent, unemotional means through which to assess the performance of strategies. Here is the list of criteria that I judge a potential new strategy by:. Notice that we have not discussed the actual returns of the strategy. Why is this? In isolation, the returns actually provide us with limited information as to the effectiveness of the strategy. They don't give you an insight into leverage, volatility, benchmarks or capital requirements.

Thus strategies are rarely judged on their returns alone. Always consider the risk attributes of a strategy before looking at the returns.

At this stage many of the strategies found from your pipeline will be rejected out of hand, since they won't meet your capital requirements, leverage constraints, maximum drawdown tolerance or volatility preferences. The strategies that do remain can now be considered for backtesting. However, before this is possible, it is necessary to consider one final rejection criteria - that of available historical data on which to test these strategies.

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.

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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.

Stratégies de trading à court terme - Linda Bradford Raschke, Laurence A.. Connors - Google Books

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.

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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.

Join the Quantcademy membership portal that caters to the rapidly-growing retail quant trader community and learn how to increase your strategy profitability.

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