Belkaglazer Researcher is a freeware soft that allows you to do some studies of the FX market. This tool may give you ideas for creating your own trading strategy.
First you will need to load a historical data. The application requires MT4 data of M1 timeframe:
The data loading might take a few minutes. The ‘Researcher’ automatically calculates the most popular timeframes (M1, M5, M10, M15, M30, H1, H2, H4, D1) from 1-minute data. The ‘Research (F5)‘ button will become active once the data file is loaded.
|If you want to…||Do this…|
|Identify a specific starting/ending date for historical data||Select a time span, choose a starting/ending date:|
|Change a timeframe||Select a specific timeframe from the list box:|
|Export historical data of the selected timeframe to a csv file (you can convert historical data from M1 to any timeframe)
||Click the ‘Save‘ button:|
|Apply filters to historical data||Click the ‘Filter‘ button:|
|Run research||Click the ‘Research (F5)‘ button:|
1. Distribution of returns
This research shows that the real distribution of price returns (changes) does not follow a normal distribution. Standard deviation (σ) is a measure of how spread out price returns are from an average value. Mean (μ) is the simple average of the price returns.
Figure 1: Distribution of returns
The price returns have a heavy-tailed (2,3) and highly-peaked (1) nature of the distribution:
A momentum strategy is based on the tails, a mean-reversion strategy uses the peaks.
2. Intraday seasonality in volatility
This research illustrates the intraday seasonal patterns in the volatility during an hour or day.
Figure 2: Average hourly volatility (New York time)
The average hourly volatility can be used to project potential amplitudes of hourly price movements. The diagram displays the regularities/trends in intraday volatility. The regularities are based on steady factors, such as opening/closing hours of the biggest financial centers or the hours at which various countries release important economic news. Different FX currency pairs are actively traded at different times of the day.
Figure 3: Volatility during an hour
Take notice of the volatility outbursts during the 0-15-30-45-59 minutes. These outbursts are due to the fact that most robots/traders use standard M15, M30, H1 timeframes.
3. High / Low of the day
The initial goal of this study is to find out when the high or low of the day will most probably occur.
Figure 4: High/Low of the day
This diagram can help estimate the probability that a trade in the chosen direction will be successful. For example, if the price has fallen to the “lower” level of a local range at the moment when the minimum of the day most probably occurs then it can be a great time to buy.
4. Weekly / monthly seasonality in volatility
The seasonality is an important factor in Forex trading. For example, the volatility can narrow during Monday and expand during other days of the week. There is a fundamental difference in how some currency pairs behave during summer, winter, and etc.
Figure 5: Weekly / monthly seasonality
5. Price Action
The study shows how the price moves (up or down) after a certain (upward or downward) price movement has occurred.
Let’s look at an example with EURUSD pair.
Figure 6: Mean reversion pattern during the first hours of the London FX Session (when the Forex market opens in London)
The green bar (1) at 8:30/9:00 am (New York time) shows that if for the last 8 bars an upward price movement has been more than 50% of the daily ATR then a price will move down (on average) by 17/13% during the next 4 bars.
The red bar (1) at 8:30/9:00 am (New York time) shows that if for the last 8 bars a downward price movement has been more than 50% of the daily ATR then a price will move up (on average) by 14/22% during the next 4 bars.
Take notice of the time after 10:30 (2). The lack of trends means that the price movement after 10:30 does not depend on the previous short-term price movements.
Now let’s try to test this simple pattern using the Belkaglazer: