The first iteration will test the volatility range between , then next between , and so on. This will give us an idea if volatility plays a part in the success of the trading model. I want to independently test the volatility filter to see how it alone affects the performance.

Below are the results of the volatility study. The x-axis contains the volatility range as measured by our calculation explained above. The y-axis contains the net profit in dollars. This is a bit choppy. There is no clear winning range. Generally speaking the most profitable bars all fall within the range of , yet, within that range there are three of the largest losing bars as well.

I went ahead and selected that range, and allowed the strategy to execute. The results are below. In the end, this does not appear to be much of a change. Moving on, I did notice something interesting when looking at the length of time between winning trades and losing trades.

Time Based Exit By reviewing the performance report of the baseline I noticed that many of the losing trades were trades that had long hold times. Winning trades were often profitable within a few bars. In so doing the trader might prevent losses that would have occurred had the trade not been executed, or force the entry into a trade that later turns out to be profitable. Equally, however, the trader might force the exit of a trade that later turns around and moves from loss into profit, or enter a trade that turns out to be a loser.

There is no way for the trader to know, ex-ante, which of those scenarios might play out. And the trader will have to face the same decision perhaps as many as twenty times a day. If the trader is really that good at picking winners and cutting losers he should scrap his trading system and trade manually!

An alternative approach would be to have the trading system handle the problem, For example, one could program the system to convert limit orders to market orders if a trade occurs at the limit price MIT , or after x seconds after the limit price is touched. Again, however, there is no way to know in advance whether such action will produce a positive outcome, or an even worse outcome compared to leaving the limit order in place. In reality, intervention, whether manual or automated, is unlikely to improve the trading performance of the system.

What is certain, however, is that by forcing the entry and exit of trades that occur around the extreme of a price bar, the trader will incur additional costs by crossing the spread. One day, I found myself asking the question: what would happen if we slowed the strategy down?

Specifically, suppose we took the 3-minute E-Mini strategy and ran it on 5-minute bars? My first realization was that the relative simplicity of alpha-generation algorithms in HFT strategies is an advantage here. In a low frequency context, the complexity of the alpha extraction process mitigates its ability to generalize to other assets or time-frames. So what happens if we run the E-mini scalping system on 5-minute bars instead of 3-minute bars?

Obviously the overall profitability of the strategy is reduced, in line with the lower number of trades on this slower time-scale. But note that average trade has increased and the strategy remains very profitable overall. Hence, not only do we get fewer, slightly more profitable trades, but a much lower proportion of them occur at the extreme of the 5-minute bars.

Consequently the fill-rate issue is less critical on this time frame. Of course, one can continue this process. What about minute bars, or minute bars? What one tends to find from such experiments is that there is a time frame that optimizes the trade-off between strategy profitability and fill rate dependency.

However, there is another important factor we need to elucidate. In fact, there are significant variations in the extreme hit rate during the course of each trading day, with rates rising during slower market intervals such as from 12 to 2pm. Wall Time vs Trade Time What we need to do is reconfigure our chart to show bars comprising a specified number of trades, rather than a specific number of minutes.

In this scheme, we do not care whether the elapsed time in a given bar is 3-minutes, 5-minutes or any other time interval: all we require is that the bar comprises the same amount of trading activity as any other bar. During high volume periods, such as around market open or close, trade time bars will be shorter, comprising perhaps just a few seconds. During slower periods in the middle of the day, it will take much longer for the same number of trades to execute.

But each bar represents the same level of trading activity, regardless of how long a period it may encompass. How do you decide how may trades per bar you want in the chart? Since volatility scales approximately with the square root of time, if we want to reduce the extreme hit rate by a factor of 2, i. So in this illustration we would need volume bars comprising 4, contracts per bar. Of course, this is just a rule of thumb — in practice one would want to implement the strategy of a variety of volume bar sizes in a range from perhaps 3, to 6, contracts per bar, and evaluate the trade-off between performance and fill rate in each case.

Using this approach, we arrive at a volume bar configuration for the E-Mini scalping strategy of 20, contracts per bar. In this scenario manual intervention is likely to have a much less deleterious effect on trading performance and the strategy is probably viable, even on a retail trading platform.

Note: the results below summarize the strategy performance only over the last six months, the time period for which volume bars are available. Concluding Remarks We have seen that is it feasible in principle to implement a HFT scalping strategy on a retail platform by slowing it down, i. The simplicity of many HFT alpha generation algorithms often makes them robust to generalization across time frames and sometimes even across assets.

An even better approach is to use volume bars, or trade-time, to implement the strategy. You can estimate the appropriate bar size using the square root of time rule to adjust the bar volume to produce the requisite fill rate. Finally, a word about data. While necessary compromises can be made with regard to the trading platform and connectivity, the same is not true for market data, which must be of the highest quality, both in terms of timeliness and completeness.

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The main difference between scalping and the other trading styles is the trading timeframe and holding period of trades. Scalping is an extremely short-term and fast-paced trading style, where traders hold trades for a few seconds to a few minutes. In order to find such short-term trading opportunities, scalpers have to rely on very short timeframes, such as the 1-minute and 5-minute ones.

Unfortunately, beginners often fall into this group of traders and start scalping the market, unaware of the risks that scalping carries. In fact, if you want to scalp the market successfully, you need to be an experienced trader. I usually recommend becoming consistently profitable with a day trading or swing trading technique before you move on to scalping.

Longer-term trading styles provide you enough room to analyse the market and avoid impulsive trades. You can look for trade setups from a safe distance when swing trading the market. Even if your analysis proves wrong, you can close a longer-term trade before it starts to make a large damage to your trading account. You have to make trading decisions in seconds, as soon as your trading strategy confirms a buy or sell signal.

Learn More: What is Day Trading? And The Main Styles Pros and Cons of Scalping Scalping carries unavoidable risks which come with trading on very short-term timeframes. Scalpers face higher trading costs than longer-term traders since they open much more trades on a daily basis. In addition, market noise and news releases can easily turn a profitable trade into a loser or even hit your stop levels.

Still, scalping can also be very profitable if you follow the rules and understand price-movements on short-term timeframes. Here are the main advantages and disadvantages of scalping. There are always trading opportunities present on the 1-minute or 5-minute charts, and new setups arise as fast as old go.

Large number of trades — Scalpers usually take a very high number of trades during a day. You may enter the trade in either of 2 ways — with a long entry or with a short entry. With the long entry, you must wait for the 3EMA to cross above the 18 Bollinger bands middle line. This is a simple but very effective trading technique.

Note that scalping usually requires a sizeable investment in order to be worthwhile. Therefore, you must be able to commit to this in order to get the best results with scalping. When to exit the trade You should exit the trade when 1 or more of the 3 conditions for entry are not satisfied.

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The first iteration will test the volatility range betweenthen next betweenand so on.

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