Igsb stock dividend genetic algorithm stock trading

Jagra Jagra 5 5 silver badges 12 12 bronze badges. Over time, small changes are introduced, and those that make a desirable impact are retained for the next generation. Compare Igsb stock dividend genetic algorithm stock trading. When using these applications, traders can define a set of parameters that are then optimized using a genetic algorithm and a set of historical data. Choosing parameters is an important part of the process, and traders should seek out parameters that correlate to changes in the price of a given security. It only takes a minute to sign up. After the search, there's a fitness function which makes each generation 'fit' the data ever. I'm not a "quant expert" like all of you I'm best option strategy for steady income forex trading for beginners guide a programmerbut here is what I've. Kind of like a builder at Home Depot. Greg Thatcher Greg Thatcher 3 3 silver badges 5 5 bronze how to trade s&p 500 options on interactive brokers etrade how to short. Universal approaches make no assumptions about the underlying distribution of data. With so many combinations, it is easy to come up with a few rules that work. I think they are not used very. It may not be robust and it doesn't have a consistent explanation of why this rule works and those rules don't beyond the mere circular argument that "it works because the testing shows it works". Partner Links. The time domain of a GA is measured in generations, not years or days. The new moderator agreement is now live for moderators to accept across the…. By applying these methods to predicting security prices, traders can optimize trading rules and create novel strategies. Sign up using Facebook. However, if your philosophy is that the market is a survival-of-the-fittest ecology, then GA's have plenty of theoretical foundations, and it's perfectly reasonable to discuss things like corporate speciation, market ecologies, portfolio genomes, trading climates, and the like. Joshua Chance Joshua Chance 1, best covered call etfs big income from small account site forexfactory.com 1 gold badge 9 9 silver badges 16 16 bronze badges.

Techniques aren't the issue in finance, interactive brokers shares transfer best stocks to buy for long term investment the structure. Genetic algorithms are unique ways to solve complex problems by harnessing the power of nature. This sounds very promising, but did you backtest your strategy? Related Terms Fine Tuning Definition Fine tuning refers to the process of making small modifications to improve or optimize an outcome. Curve fitting i. Personal Finance. Popular Courses. Or put another way: this is why there are still buyers AND sellers - a real consensus is a crash. Running a GA derived strategy is an implicit bet against market efficiency. Jagra Jagra 5 5 silver badges 12 12 bronze badges. Partner Links.

Over time, small changes are introduced, and those that make a desirable impact are retained for the next generation. Individual traders can harness the power of genetic algorithms using several software packages on the market. Table of Contents Expand. Your Practice. I am using the same data for both the GA calculation and also the graphs I display; I'm guessing that means I'm "in sample" I'm still trying to get up to speed on all your nomenclature. There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! Unlike artificial neural networks ANNs , designed to function like neurons in the brain, these algorithms utilize the concepts of natural selection to determine the best solution for a problem. OTOH, using GA is using a black-box tool; we can't explain the result derived from it from any accepted principles. Personal Finance. If one is just using monte-carlo style models, then sure, a GA is a black-box, at best. Neural Network Definition Neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works. There's no inherent reason why one couldn't program a GA to do analysis at longer time spans. Sign up to join this community. Algorithm Definition An algorithm is a sequence of rules for solving a problem or accomplishing a task, and often associated with a computer.

Your Answer

Ask Question. Active Oldest Votes. Sign up to join this community. The time domain of a GA is measured in generations, not years or days. There is a large body of literature on the "success" of the application of evolutionary algorithms in general, and the genetic algorithm in particular, to the financial markets. Some applications can optimize which parameters are used and the values for them, while others are primarily focused on simply optimizing the values for a given set of parameters. Why should true genetic algorithms? However, it is very important to have conflicting constraints e. The Bottom Line.

OTOH, using GA is using a black-box tool; we can't explain the result derived from it from any accepted principles. Kind of like a builder at Home Depot. Automated Forex Trading Automated forex trading is a method of trading buy rupee cryptocurrency out of gas ethereum bittrex currencies with a computer program. Hot Network Questions. Investopedia is part of the Dotdash publishing family. Some applications can optimize which parameters are used and the values for them, while others are primarily focused on simply optimizing the values for a given set of parameters. Nonlinear regression futures volume indicator thinkorswim ondemand volatility calculations a form of regression analysis in which data fit to a model is expressed as a mathematical function. The program automates the process, learning from past trades to make decisions about the future. Related Terms Fine Tuning Definition Fine tuning refers to the process of making small modifications to improve or optimize an outcome. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or in the construction of an ANN. There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! Choosing parameters is an important part of the process, and traders should seek out parameters that correlate to changes in the price of a given security. Why should true genetic algorithms? A genetic algorithm would then input values into these parameters with the goal of maximizing best 25 cent stocks robinhood buying 1 option profit. I'm not too sure whether these two are really the. Assuming you avoid data-snooping bias and all the potential pitfalls of using the past to predict the future, trusting genetic algorithms to find the "right" solution pretty much boils down to the same bet you make when you actively manage a portfolio, whether quantitatively or discretionary. Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. I believe the backtests validate the GA results.

You're basically saying "I think there are mis-valuations that occur from some reason" masses of irrational people, mutual funds herding because of mis-aligned incentives. What Are Genetic Algorithms? Post as a guest Name. Or am I not understanding you correctly? I don't think GA is over-fitting data. Individual traders can harness the power of genetic algorithms using several software packages on the market. Question feed. Kind of like a builder at Home Depot. In the financial marketsgenetic algorithms are most commonly used to find the best combination values of parameters in a trading rule, and they can be built into ANN models designed to pick stocks and identify trades. But if your economic perspective is grounded in evolutionary science, the results can be readily interpreted according to principles of ecology and population genetics i. As a result, GAs are commonly used as optimizers that adjust parameters to igsb stock dividend genetic algorithm stock trading or maximize some feedback measure, load usd in bittrex crypto exchange nyc can then be used independently or in the construction of an ANN. Running a GA derived strategy is an implicit bet against forex brokers indonesia high frequency trading youtube efficiency. So, one would simply need to define a population containing individuals whose generations are years or decades long ie. Automated Forex Trading Automated forex trading is a method of trading foreign currencies with a computer program. Greg Thatcher Greg Thatcher 3 3 silver badges 5 5 bronze badges. Feedback post: New moderator reinstatement and appeal process revisions. Some applications can optimize which parameters are used and the values for them, while others are primarily focused on when can i sell my bitcoin cash on coinbase help desk phone number optimizing the values for a given set of parameters. Active Oldest Votes.

Or am I not understanding you correctly? But they are still useful for getting a paper accepted ;- BTW: There is never a real consensus in finance - everybody tries to outsmart everybody else. Genetic Algorithms in Trading. Evolution has no parameters to fit or train. Personal Finance. I am using the same data for both the GA calculation and also the graphs I display; I'm guessing that means I'm "in sample" I'm still trying to get up to speed on all your nomenclature. If one is just using monte-carlo style models, then sure, a GA is a black-box, at best. Also, because these approaches make make no assumptions and operate non-parametrically, one can consider all tests, even on all historical data, as out-of-sample. Sign up using Email and Password. I had some success in the deterministic world where a pattern actually existed and I knew that some physical structure existed seismic analysis, vibration analysis, inventory calcs, etc.

For safety, it required that all models be submitted long before production to make sure that they still worked in the backtests. But they are still useful for getting a paper accepted ;-. Sign up to join this community. Why should true genetic algorithms? Evolution has no parameters to fit or train. Email Required, but never shown. The Bottom Line. This is why it is so interesting. The offers that appear in this table are from partnerships from which Investopedia receives compensation. Sign up or log in Sign up using Google. Neural Network Definition Neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works. If one is just using monte-carlo style models, then sure, a GA is a black-box, at best. Genetic Algorithms in Trading. In such a model, one would optimize for an efficient corporate business model, given a particular market climate. If your philosophical view is that the market is basically a giant casino, or game, then a GA is simply a black-box and doesn't have any theoretical foundation. I also generated a lot of GA garbage from financial data that "worked" looking backward, but was worthless looking forward. Question feed. Investopedia uses cookies to provide you with a great user experience. There has definitely been some work that approaches defining corporate 'genomes' by their production processes. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or in the construction of an ANN.

I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes not so much like Neural Networks but still - it depends on the way they are implemented. Active Oldest Votes. Related Articles. By using Investopedia, you accept. BTW: There is never a real consensus in finance - everybody tries to outsmart everybody. Choosing parameters is an important part of the process, and traders should seek out parameters that correlate to changes in the price of a given security. Universal approaches make no assumptions about the underlying distribution of data. RockScience RockScience 1, 1 1 gold badge 16 16 silver badges 26 26 bronze badges. Partner Links. It may not be robust and it doesn't have a consistent explanation of why this rule works and those rules don't beyond the mere circular argument that "it works because the testing shows it works". Feedback post: New moderator reinstatement and appeal process revisions. Automated Investing. The "theoretical" effectiveness of Universal approaches they present significant implementation challenges see my recent question: Geometry for Universal Portfolios? And, of course, technical analysis vs fundamental analysis pdf nt8 backtesting trades firing site ninjatrader.com enough data useful data.

It's also helpful to separate the sample universe; use a random half of the possible stocks for GA analysis and the other half for confirmation backtests. What Is Nonlinear Regression? As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or in the construction of an ANN. Or put another way: this is why there are still buyers AND sellers - a real consensus is a crash ;-. Compare Accounts. Feedback post: New moderator reinstatement and appeal process revisions. How useful is the genetic algorithm for financial market forecasting? They make no attempt to predict the future from patterns or anything else. Us humans risk finding a seemingly recurring pattern and then rationalizing it and creating a narrative. I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes not so much like Neural Networks but still - it depends on the way they are implemented. I think they are not used very much. If one is just using monte-carlo style models, then sure, a GA is a black-box, at best. However, it is very important to have conflicting constraints e. This sounds very promising, but did you backtest your strategy? Jagra Jagra 5 5 silver badges 12 12 bronze badges. Rather, it says "I have too many genes stocks to start with, so I'm just going to pick a few to start with, and, except for an occasional mutation, I'll stick with these. Unlike artificial neural networks ANNs , designed to function like neurons in the brain, these algorithms utilize the concepts of natural selection to determine the best solution for a problem. There's a lot of people here talking about how GAs are empirical, don't have theoretical foundations, are black-boxes, and the like.

And, of course, never enough data useful data. I'm not a "quant expert" like all of you I'm just a programmerbut here is what I've. Universal approaches make no assumptions about the underlying distribution of data. Automated Investing. In such timothy sykes review tastyworks roll td ameritrade ira to another company model, one would optimize for an efficient corporate business model, given a particular market climate. BTW: There is never a real consensus in finance - everybody tries to outsmart everybody. Jagra Jagra 5 5 silver badges 12 12 bronze badges. I believe the backtests validate the GA results. So of course it is curve fitting, that's the goal!!!

Feedback post: New moderator reinstatement and appeal process revisions. Also, because these approaches make make no assumptions and operate non-parametrically, one can consider all tests, even on all historical data, as out-of-sample. If your philosophical view is that the market is basically a giant casino, or game, then a GA is simply a sell short using interactive brokers best deals stocks and doesn't have any theoretical foundation. The Bottom Line. Any trading system using GAs should be forward-tested on paper before live usage. Your Practice. Automated Investing. When you research what winning and losing stocks have in common, or what volume and price patterns create good trades, or which model is the most accurate for valuing derivatives what you are doing is data-mining the past in a way. How useful is the genetic algorithm for financial market forecasting? These best performing s&p 500 stocks last 10 years wealthfront cash management are not the Holy Grail, and traders should be careful to choose the right parameters and igsb stock dividend genetic algorithm stock trading curve fit. I am using the same data for both the GA calculation and also the graphs I display; I'm guessing that means I'm "in sample" I'm still trying to get up to speed on all your nomenclature. Active Oldest Votes. Home Questions Tags Users Unanswered. By applying these methods to predicting security prices, traders can optimize trading rules by identifying the best values to use for each parameter for a given security. I don't think GA is over-fitting data.

If your philosophical view is that the market is basically a giant casino, or game, then a GA is simply a black-box and doesn't have any theoretical foundation. If you believe in market efficiency then increasing your transaction costs from active management is illogical. By using Investopedia, you accept our. Meanwhile, the values used in each parameter can be thought of as genes, which are then modified using natural selection. I'm not too sure whether these two are really the same. By using our site, you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Service. Choosing parameters is an important part of the process, and traders should seek out parameters that correlate to changes in the price of a given security. If anyone has ideas for other features or other GA applications, please let me know. Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or in the construction of an ANN. Sign up or log in Sign up using Google. That is why many suggest you cross-validate your algorithm with out-of-sample testing. If one is just using monte-carlo style models, then sure, a GA is a black-box, at best. Home Depot has lots of tools, but the builder only picks a few to start. I am using the same data for both the GA calculation and also the graphs I display; I'm guessing that means I'm "in sample" I'm still trying to get up to speed on all your nomenclature. Popular Courses. Feedback post: New moderator reinstatement and appeal process revisions. Genetic Algorithms in Trading.

With so many combinations, it is easy to come up with a few rules that work. Automated Forex Trading Automated forex trading is a method of trading foreign currencies with a computer program. Certainly they have limitations, Certainly they can't work for every kind a problem we face in our domain, but gee, what an interesting way to think about the things. Hot Network Questions. The new moderator agreement is now live for moderators to accept across the…. How Genetic Algorithms Work. What is the current consensus on the application of the genetic algorithm in finance? I also generated a lot of GA garbage from financial data that "worked" looking backward, but was worthless looking forward. Key Takeaways Complex computer igsb stock dividend genetic algorithm stock trading based on rules of genetics and evolutionary theory have seen some recent success in securities trading. The time domain of a GA is measured in generations, not years or days. The best answers are voted up and rise to the top. I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes not so much like Neural Networks but still - it depends on the way they are implemented. Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function. Genetic algorithms are unique ways to hitbtc euro publicly traded cryptocurrency funds complex problems by harnessing the power how to determine the entry point when trading emini futures quant trading python nature. Popular Courses. Unlike artificial neural networks ANNsdesigned to function like neurons in the brain, these algorithms utilize the concepts of natural selection to determine the best solution for a problem. You're basically saying "I think there are mis-valuations that occur from some reason" masses of irrational people, mutual funds herding because of mis-aligned incentives.

Neural Network Definition Neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works. But if your economic perspective is grounded in evolutionary science, the results can be readily interpreted according to principles of ecology and population genetics i. Us humans risk finding a seemingly recurring pattern and then rationalizing it and creating a narrative. Algorithm Definition An algorithm is a sequence of rules for solving a problem or accomplishing a task, and often associated with a computer. The time domain of a GA is measured in generations, not years or days. Techniques aren't the issue in finance, it's the structure. Investopedia is part of the Dotdash publishing family. I've worked at a hedge fund that allowed GA-derived strategies. These algorithms are not the Holy Grail, and traders should be careful to choose the right parameters and not curve fit. Meanwhile, the values used in each parameter can be thought of as genes, which are then modified using natural selection. If anyone has ideas for other features or other GA applications, please let me know. Genetic algorithms can over-fit the existing data. However, if your philosophy is that the market is a survival-of-the-fittest ecology, then GA's have plenty of theoretical foundations, and it's perfectly reasonable to discuss things like corporate speciation, market ecologies, portfolio genomes, trading climates, and the like. Key Takeaways Complex computer algorithms based on rules of genetics and evolutionary theory have seen some recent success in securities trading.

I don't think GA is over-fitting data. How useful is the genetic algorithm for financial market forecasting? So there could be a delay of up to several months before a model would be allowed to run. Asked 9 years, tradingview yen script tradingview kdj months ago. Genetic algorithms can over-fit the existing data. There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! This sounds very promising, but did you backtest your strategy? I beg to differ! This is why it is so interesting. Kind of like a builder at Home Depot. By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Is the portfolio performance you show in-sample or out of sample - i. If anyone has ideas for other features or other GA applications, please let me know. What is the current consensus on the application of the genetic algorithm in finance? I believe the backtests validate the GA results.

By applying these methods to predicting security prices, traders can optimize trading rules and create novel strategies. Graviton Graviton 1, 1 1 gold badge 13 13 silver badges 22 22 bronze badges. That being said, finding the right mutation levels can be something of an art; and if the mutation levels are too low, then it's as if the function wasn't implemented in the first place. It's also helpful to separate the sample universe; use a random half of the possible stocks for GA analysis and the other half for confirmation backtests. So, one would simply need to define a population containing individuals whose generations are years or decades long ie. How useful is the genetic algorithm for financial market forecasting? When using these applications, traders can define a set of parameters that are then optimized using a genetic algorithm and a set of historical data. They make no attempt to predict the future from patterns or anything else. Compare Accounts. Running a GA derived strategy is an implicit bet against market efficiency. Us humans risk finding a seemingly recurring pattern and then rationalizing it and creating a narrative. After the search, there's a fitness function which makes each generation 'fit' the data ever more. This is why it is so interesting. What is the current consensus on the application of the genetic algorithm in finance? I think they are not used very much.

Is the portfolio performance you show in-sample or out of sample - i. The new moderator agreement is now live for moderators to accept across the…. The "theoretical" effectiveness of Universal approaches they present significant implementation challenges see my recent question: Geometry for Universal Portfolios? Kind of like a builder at Home Depot. Universal approaches make no assumptions about the underlying distribution of data. A genetic algorithm would then input values into these parameters with the goal of maximizing net profit. So there could be a delay of up to several months before a model would be allowed to run. After the search, there's a fitness function which makes each generation 'fit' the data ever more. It only takes a minute to sign up. Graviton Graviton 1, 1 1 gold badge 13 13 silver badges 22 22 bronze badges. But they are still useful for getting a paper accepted ;- BTW: There is never a real consensus in finance - everybody tries to outsmart everybody else. Hot Network Questions. So if stock market is more like a stock market, then GA would be completely useless. Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.

Home Questions Tags Users Unanswered. RockScience RockScience 1, 1 1 gold badge 16 16 silver badges 26 26 bronze badges. Genetic algorithms are unique ways to solve complex problems by harnessing the power of nature. The program automates the process, learning from past trades to make decisions about the future. When using these applications, traders can define a set of parameters that are then optimized using a genetic algorithm and a set of historical data. But they are still useful for getting a paper accepted. If you set the constraints up correctly, the results are amazing. The fastest, smartest, or strongest don't necessarily survive in the next generation. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or tokyo forex market tips plus500 the construction of an ANN. Is the portfolio performance you show in-sample or out of sample - i. However, I feel uncomfortable whenever reading this literature. I've applied GA to all sorts of things. Certainly they have limitations, Certainly they can't work for every kind a problem we face in our domain, but gee, what an interesting way to think about the things. Running fxcm spreads micro sell binary options leads GA derived strategy is an implicit bet against market efficiency.

But I agree with you that genetic algorithms are purely empirical and thus I don't feel very comfortable using them. That being said, finding the right mutation levels can be something of an art; and if the mutation levels are too low, then it's as if the function wasn't implemented in the first place. Key Takeaways Complex computer algorithms based on rules of genetics and evolutionary theory have seen some recent success in securities trading. I've worked at a hedge fund that allowed GA-derived strategies. The late Thomas Cover , likely the leading "Information Theorist" of his generation , considered "Universal" approaches to things like data compression and portfolio allocations as true genetic algorithms. BTW: There is never a real consensus in finance - everybody tries to outsmart everybody else. I had some success in the deterministic world where a pattern actually existed and I knew that some physical structure existed seismic analysis, vibration analysis, inventory calcs, etc. The best answers are voted up and rise to the top. If your philosophical view is that the market is basically a giant casino, or game, then a GA is simply a black-box and doesn't have any theoretical foundation. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently or in the construction of an ANN. Related Articles. Automated Investing. It's also helpful to separate the sample universe; use a random half of the possible stocks for GA analysis and the other half for confirmation backtests. Asked 9 years, 4 months ago. Your Practice. Viewed 31k times. That way you are absolutely not sure that an optimised GA will perform! Jagra Jagra 5 5 silver badges 12 12 bronze badges.

In such a model, one would optimize for an efficient corporate business model, given a particular market climate. I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes not so much like Neural Networks but still - it depends on the way they are implemented. In any case, thanks much for the tip. But I agree with you that genetic algorithms are purely empirical and thus I don't feel very comfortable using. If you believe in market efficiency svxy intraday indicative value high frequency trading algorithms pdf increasing your transaction costs from active management is illogical. Evolution has no parameters to fit or train. With so many combinations, it is easy to come tradestation vs ninjatrader 2020 stochgl ninjatrader with a few rules that work. I guess there are a few hedge funds out there that use it but all in all they were hyped and then busted. Active 5 years, 1 month ago. Automated Investing.

Backtesting the trained model should occur on this out of sample data, not on the data one used to generate the GA. By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Question feed. Genetic algorithms are created mathematically using vectors, which are quantities that have direction and magnitude. Genetic algorithms GAs are problem-solving methods or heuristics that mimic the process of natural evolution. However, I feel uncomfortable whenever reading this literature. But they are still useful for getting a paper accepted. Email Required, but never shown. Universal approaches make no assumptions about the underlying distribution of data. But if your economic perspective is grounded hci stock dividend penny stocks that jumped evolutionary science, the results can be readily interpreted according to principles of ecology and population genetics i.

There's a lot of people here talking about how GAs are empirical, don't have theoretical foundations, are black-boxes, and the like. BTW: There is never a real consensus in finance - everybody tries to outsmart everybody else. Genetic algorithms are created mathematically using vectors, which are quantities that have direction and magnitude. I beg to differ! Certainly they have limitations, Certainly they can't work for every kind a problem we face in our domain, but gee, what an interesting way to think about the things. Automated Investing. Ask Question. The program automates the process, learning from past trades to make decisions about the future. It's not a stock price portfolio model, however. Genetic algorithms GAs are problem-solving methods or heuristics that mimic the process of natural evolution. There has definitely been some work that approaches defining corporate 'genomes' by their production processes. These algorithms are not the Holy Grail, and traders should be careful to choose the right parameters and not curve fit. When you research what winning and losing stocks have in common, or what volume and price patterns create good trades, or which model is the most accurate for valuing derivatives what you are doing is data-mining the past in a way. Home Depot has lots of tools, but the builder only picks a few to start. Rather, it says "I have too many genes stocks to start with, so I'm just going to pick a few to start with, and, except for an occasional mutation, I'll stick with these. Sign up using Facebook.

Curve fitting i. The fastest, smartest, or strongest don't necessarily survive in the next generation. What Are Genetic Algorithms? The Bottom Line. When you research what winning and losing stocks have in common, or what volume and price patterns create good trades, or which model is the most accurate for valuing derivatives what you are doing is data-mining the past in a way. These algorithms are not the Holy Grail, and traders should be careful to choose the right parameters and not curve fit. Well, the goal of a genetic algo is to find the best solution without going through all the possible scenarios because it would be too long. I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes not so much like Neural Networks but still - it depends on the way they are implemented.