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It only takes a minute to sign up. Connect and share knowledge within a single location that is structured and easy to search. I trade use a completely automated approach where all signals are generated by proprietary trading strategies. However, recently I encountered an challenging problem:.
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The question is how to make the best use of capital to maximize the portfolio returns. This is effectively the weighted average approach. It does have the merit of diversification, but the cash is undoubtedly utilized inefficiently. This is in fact the lower bond of portfolio returns. Another approach is to try to share the risk capital among the 3 strategies in some efficient way. In reality, 3 strategies tend to have trades that set up together. So, what do you think is the optimal solution for achieving maximum capital utilization?
Svisstack has provided a good starting point for discussion. Now, I will refine my questions based on his reply:. How to combine signals efficiently when there are N strategies, shall we keep capital fully invested while weighting each strategy by their performance? What type of strategies, if combined together, can yield maximum benefits? For example, shall we combine two strategies that are both based on similar hypothesis, similar holding period, or two strategies that rarely traded together regardless of other characteristics?
Optimal scenario should use all available capital for 3 strategies, in way that when only 1 strategy trading then using all available capital, when second trying to create trade while first taken all capital, then half of capital can be moved from 1 strategy to cover 2 strategy trades or 2strategy wait for release capital from 1 strategy. When third strategy try to get in then same, should wait for capital release or capital allocation from 1 and 2 strategy should be moved to cover third strategy trades in proper ratio.
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In your scenario when 3 strategies have same performance estimation, capital should be spitted equally between active trading strategies at every moment. This should work well enough, though I would recommend also implementing Idzorek's extension for numerically calculating the appropriate omega matrix based on view confidence as a percentile:. In Forex investments, the leverage is any technique involving the use of borrowed funds in the purchase of an asset. Online brokers offer their clients leverage.
This tool actually allows the speculation with more money than the capital available in order to make the benefits more interesting.
Currency exchange rate fluctuations are often very low. Without leverage, it would be very difficult to make profits, even with important investment capital. The investment sequences are presented in Figure 7. First, we will invest only in weeks with positive trends and in each positive week we will check for next day positive trend to trade.
This way, we can reduce the number of false investment rates. The strategy can be described as follows. The sequences of the proposed investment strategy. Step 1. For each currency we check for the week positive trend using the following rules: i Based on technical indicators, we check the market status for one of these situations [ 65 , 66 ]: a The oversold situation: it is a situation where the price of an asset has fallen sharply to a level below its real value.
It is a sign and probably the price should rebound. Probably it is an indication to sale. The indicator indicates an increase in the price of the asset, while the asset continues to fall. This can be analyzed by a possible reversal of the upward trend and by a future buy signal. This is analyzed by a reversal of the downtrend and by a sales signal to come.
Step 2. Step 3. We tested our investments strategy over 17 weeks and two years data from January to January to train our algorithms. For final results we calculate the cumulated gain over 17 weeks. The tables reveal that the proposed system demonstrates better results than Random Forest or Probit regression. To validate our model, we choose to evaluate its efficiency over three currency pairs and for the three pairs the proposed strategy shows the best results.
In addition, the proposed system needs less investment to make more benefit. The proposed model produces a quite promising profit with an average profit of 4. We used only weeks with positive trends. The proposed system allows us to reduce the number of daily investment without losing profit opportunity. The true positive measures the proportion of actual positives that are correctly identified. We should clarify that the previous results influenced the currency pair global trend during the next six months.
This means it is related to macroeconomic and political situation. It was clear that we had a sideways trend. A sideways trend is a horizontal price movement. We also benefit from the fact that currency market is relatively stable and changes of more than even one percent are rare.
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Simultaneously, an important issue that has not been mentioned so far is the trading cost. For each transaction, the currency market is a commission-free market. Instead of a commission, there is a pip spread. A pip spread is the difference between selling and buying price in the same moment. We consider a commission of 1 pip.
From that fact and when using a leverage, we deduce that mostly some currency pairs resulted in modest gains and some resulted in excessive losses; an excessive gain is really rare. Generally, Forex traders act emotionally with fear and hope.
Naive Bayes classifier for signals of a set of indicators - MQL5 Articles
Through this work, we presented a trading strategy that allows putting emotions aside, avoiding trading errors greed, panic, or doubt and not missing the trading opportunities. Clearly our strategy gives inputs and outputs signals when the predefined rules coincide. In this moment, our system is triggering regardless of sentiment and performance of the last losing or winning position.
The results presented in this work show the benefits of our system compared to a simple use of regression or classification using Random Forest. Taking into account the obtained results, using a combination of classification and regression trees can be implemented as a successful algorithmic trading system. Our results indicate that further research on the consecutive combination of many algorithms for Forex portfolio management is useful.
Heiken Ashi Naïve
This combination helps traders to determine the moment when we can buy or sell the currency pair. In Forex there are many currency pairs and many trading people and each pair is different from the other, and each person thinks in his own way. Finding the best trading strategy is really a complex preoccupation. In order to find an adequate solution, we have presented in this study a new strategy based on two data mining algorithms.
Our approach was to introduce a prediction and decision model that produces profitable intraweek investment strategy. The proposed strategy allows improving trading results in intraweek high-frequency trading.
Crypto quant trading: Naive Bayes
The results of the performed tests have demonstrated considerable advantage of our system versus a simple use of regression or classification using Random Forest. Such results are promising for research on consecutive combination of many algorithms to Forex portfolio management. It is concluded that algorithmic trading based on combination of classification and Probit regression can be effective in improving the prediction accuracy. This combination helps to identify the good times to buy or to sell currency pairs.
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The proposed system, based on this combination, helps traders to take profit from the many opportunities on the Forex market. The data used to support the findings of this study are available from the corresponding author upon request. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Journal overview. Special Issues. Academic Editor: Miin-Shen Yang. Received 18 Mar Revised 07 Jul Accepted 25 Jul Published 27 Aug Abstract In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Introduction The strong fluctuations in the financial markets make the stock market a risky area for investors.
In this paper, we propose a secured investment strategy in two stages: Firstly, we have opted for a temporal approach without any prescriptive hypothesis on financial market trends. Related Work Developments in the algorithm trading have improved recently. Trading Algorithms Approaches Trading strategy is an important financial method.
Artificial Neural Networks Approaches Neural Networks are a key topic in several papers in order germane to trading systems. Genetic Algorithms Approaches Genetic algorithms GA , developed by Holland [ 39 ], are a type of optimization algorithms and they are used to find the maximum or minimum of a function. Probit Model Our choice is the Probit model, which is a type of regression where the dependent variable can take only two values, for our case increased 1 or decreased 0 value of currencies [ 53 ].
The observed binary variable is defined by where the unobserved effect and the general error term. In the Probit model case, the cumulative distribution is a standard normal: The first equality states that is assumed to be strictly exogenous conditional on. Random Forest algorithm: Input: description language; sample S Begin Initialize to the empty tree; the root is the current node Repeat Decide if the current node is terminal If the node is terminal then Assign a class Else Select a test and create the subtree End if Move to the next node unexplored if there is one Until you get a decision tree End Decision trees provide effective methods that work well in practice.
Figure 1. Figure 2. Figure 3. Figure 4. Investment strategy proposed in [ 7 ] for intraday foreign exchange.