So you need to spend some time:
1. Please read: https://belkaglazer.com/en/belkaglazer-en/how-to-use-en
2. Try to backtest strategies. It is not a good idea to use all presets at the same time. Try to choose strategies/presets and create your own portfolio in accordance with your desired values of the profit/risk. You can use the MM calculator to determine the MM settings before creating a portfolio in QuantAnalizer (this is a software for merge different MT4 reports). You need to divide trading risks between presets that use the same strategy.
3. Send me your portfolio. I can advise you how to make your portfolio better.
No one can do this work for you, because everyone has different preferences regarding the risk, profit, and strategies.
If you don’t have enough experience you can send me a PM. I will help you with your MM settings.
The description of all my signals has a list of the used presets with MM settings:
The default settings of the EA correspond to the ‘Daily_levels‘. It’s a breakout strategy that uses horizontal levels on D1. This is a very slow and long-term strategy. In the long-term, this one is the most robust strategy that I have ever known.
1. It is necessary to choose strategies with a good performance which you like (don’t forget, a spoonful of tar can spoil a whole barrel of honey). The more strategies you use, the higher the level of diversification, and the lower the yield, max. drawdown, and volatility of a portfolio.
The more strategies you use, the less you will be exposed to the risk in the long-term and the better you will be able to trade in different regimes of the market.
2. It is necessary to allocate a certain trading size (for example 0.04lots per $1000) for each strategy. Next, it is needed to divide that trading size between the pairs / presets that you are going to use. For example, if you will use 4 presets then the trading size for each pair can be 0.01lots per $1000 (0.04/4 = 0.01).
3. I don’t recommend giving a significant trading advantage (for example, the larger trading size) to one of the strategies used.
4. It is desirable that the yield ratio of M/MR strategies in a portfolio will be at least 70%/30%. It is best that it is at 50%/50%.
5. A portfolio could withstand (without significant losses) a long losing streak (this will necessarily happen in the future). If you lose 50%, then to recover losses you must make 100%.
A correct backtest of a trading strategy requires accurate historical data. Most brokers don’t have good historical data when downloading it from MT4. They provide data of poor quality from Metaquotes.
I know 2 ways to test with a high quality.
1) Using the TDS2 software with real tick data from Dukascopy and real variable spreads (it is the best way, but the TDS2 is not for free).
2) Using Alpari MT4 (free). Alpari provides some of the best historical data. They close daily charts at 5 pm New York time. Just open a demo account through MT4 and download their data via ‘History Center’.
The value of each parameter of a Model should have a rationale. It is needed to optimize only one parameter at the same time.
A robust parameter of a model can behave in 3 ways while optimizing:
1. It can work in a very wide range of values, without a significant impact on the performance of a strategy. But if we turn it off, then a strategy will stop working.
2. It can work as a filter. But if we turn it off, then a strategy will stop working.
3. It can work like this:
If a robust strategy has failed and no longer makes money, optimization cannot revive it because the market inefficiency (that is used by a strategy) has disappeared.
The filter values should be selected based on the basic performance of a model: trades/PF/avg.trade. A filter cannot be optimized, by its definition. Filtering of trades is just a way to improve the model’s performance. Filtering reduces the number of trades and increases the profit factor and avg trade. Filters cannot turn a losing strategy into a winning strategy. A robust strategy should be profitable without using any filter.
A robust parameter of a filter can behave only like this:
The difference between the model and filter parameters is very simple:
If you exclude a filter component, then a strategy will show less performance, but it will still work. If you exclude a model component, then a strategy will stop working, and the equity curve will turn into a random walk. Therefore, parameters of a model are the basis of a strategy.
The mathematical (statistical) advantage of entries (the Edge of a strategy) decreases with the position holding time. So It is needed to use a time-stop (see ‘StopBar’ and ‘StopHour’ parameters). You can understand how it works if you will try to optimize it.
The longer the holding time, the higher the trading risk.
The blind optimization (without any rationale) of a robust strategy does not really make any sense.
The start date and min. period of a backtest depend on many factors.
In general, a good backtest should have at least 300-500 (it can be without filters) trades over 5-10 years. Otherwise, the results of a test will not have statistical significance.
If you want to use a preset file on another timeframe, then you should adjust a lot of settings.
If you think that your trades doesn’t match with a backtest, please, send me a screenshot with your trades through Private Message. I will compare the trades and tell you if I see something wrong.
You can also follow my signal to see the trades.
Alpari, AxiTrader, Tickmill, Pepperstone, ICMarkets and many others close daily candles at 5 pm New York time (it corresponds to ‘GMT+2’ in winter and ‘GMT+3’ in summer) and give five 24-hour daily candles for every week.
It means that you only need to set the ‘NYCloseBroker’ parameter of the EA to ‘true’ and in this case, the EA will not take into account the GMT_offset/Daylight_Saving_Time parameters. The EA will use daily charts/data directly.
If the server time of a broker is not set to the ‘New York Close’ timezone then it is necessary to set the ‘NYCloseBroker’ parameter to ‘false’ and set the GMT_offset/Daylight_Saving_Time parameters according to the time zone of your broker. In this case, the EA will recalculate daily charts and data to get five daily candles per a week.
The ‘NY_CloseTrading‘ is a mean reversion strategy based on the following simple rules:
1. The price always (sooner or later) returns to its average value calculated for a certain period.
2. A position is opened during periods of low volatility and when the maximum/minimum of the day most likely occurs.
3. It’s necessary to use pairs that often cross the average value.
4. If the price has not crossed the average value for a long time (up to ~12 hours), then such a position must be closed by a time-stop. As a rule, a position holding time does not exceed 12 hours.
5. A position should be closed after the price has crossed or approached the average value, so the profit can be both positive and negative. A stop loss value should not prevent a strategy from closing a position at the average price. The SL has to withstand Intraday fluctuations and correspond to the volatility of a currency pair.
6. Stop loss protects against big losses during huge price movements in one direction when the price does not cross the average value for a long time. Such a Stop loss is reached infrequently, it performs an insurance role.
Thus, if you use a small Stop Loss, then this will turn a profitable mean reversion strategy into an unprofitable system.