An investmentfonds wikipedia free fund also index tracker is a mutual fund or exchange-traded fund ETF designed to follow certain preset rules so that the fund can track a specified basket johann pfeiffer iforex underlying investments. Index funds may also have rules that screen for social and sustainable criteria. An index fund's rules of construction clearly identify the type of companies suitable for the fund. Additional index funds within these geographic markets may include indexes of companies that include rules based on company characteristics or factors, such as companies that are small, mid-sized, large, small value, large value, small growth, large growth, the level of gross profitability or investment capital, real estate, or indexes based on commodities and fixed-income. Companies are purchased and held within the index fund when they meet the specific index rules or parameters and are sold when they move outside of those rules or parameters. Think of an index fund as an investment utilizing rules-based investing.

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A random forest algorithm may discover the hypothetical relationship between VIX, 1-day SPY return, …, and whether your short vol trade will be profitable as illustrated in this schematic diagram:. Certainly faster than a pizza delivery. Figure 5: Choosing training mode at predictnow. Figure 6: Uploading training data. Figure 7: Choosing hyperparameters for building random forest.

Once this random forest is built trained with historical data, it is ready for your live trading. Figure 8: Live trading input. Notice that the format of this spreadsheet is the same as the training data, except that there is no known Return of course - we are hoping to predict that! Figure 9: Live prediction. One of the output files left in Figure 10 tells you the most likely outcome of your trade: profit or not. The other file right one in Figure 10 tells you the probability of that outcome.

You can use that probability to size your trade. For example, you may decide that if the probability of profit is higher than 0. But if the probability is between 0. Typically the live prediction will take 1 second or less, while the training which may not need to be re-done more than once a quarter typically won't take more than 15 minutes even for thousands of rows of historical data with features.

You can make live predictions as frequently as you like i. But predicting the conditional probability of profit for your next trade is not all that we can do. We can also tell you what features are important in making that prediction. In fact, you may be more interested in that than a black-box prediction, because this list of important features, sorted in decreasing order of importance, may help you improve your underlying simple trading strategy.

In other words, it can help improves your intuition about what works with your strategy, so you can change your trading rules. Going back to our example, predictnow. Figure Features with decreasing importance. You can see that VIX was deemed the most important feature, followed by 1-day SPY return, the latest interest rate change, and so on. Our internal predictive algorithm will actually remove all features that are "below average" and retrain the model, but you may benefit from incorporating just VIX and 1-day SPY return in your simple strategy when it generates a trading signal.

Remember, your simple strategy does not need to be an algorithmic strategy. It could be discretionary. Of course it can: you only need to pretend that your strategy is buy-and-holding the market. It can even predict the magnitude, not just the sign, of the return.

In these usages, there are no adversaries your fellow traders that are trying their hardest to arbitrage away your trading alpha, so these predictions will be more likely to work far into the future. For machine learning mavens, you may wonder why we have only implemented random forest learning algorithm.

The beauty of random forest is that it is simple, but not too simple. Complicated deep learning algorithms such as LSTM can indeed take into account the time series dependence of the features and labels more readily, but they run serious risk of data snooping due to the large number of parameters to fit. GPT-3, the latest and hottest deep learning algorithm for natural language processing, for example, has more than billion parameters to fit.

Imagine fitting that to 1, historical trades! So does this stuff really work? In retrospect, that made sense because Tail Reaper is a crisis alpha, tail hedge strategy. There was no crisis, no tail movement, from which to reap profits in those calm months. But suddenly, starting on February 1, , this machine learning program told us to expect a crisis. We thought the machine learning program was nuts - there were just a handful of Covid cases in the US at that time!

Nonetheless we followed its advice and restarted Tail Reaper. Past performance is not necessarily indicative of future results. For detailed disclosure of this strategy, please visit qtscm. Figure Tail Reaper equity curve. Thursday, August 06, What is the probability of profit of your next trade? Introducing PredictNow. Figure 4: Example classification tree generated by predictnow.

Thanks Hank. Hi Hank, Thanks. Yours is a good question, and I have not tried to control for price momentum in this study. I do expect positive correlation between contemporaneous price return and order flow in the past period.

However, from a trading perspective, those occasions when one reverts in the next period and the other doesn't that are most interesting and profitable. The theory is that price change in the absence of strong order flow will likely mean revert i. Quick question. In previous comments you mentioned that your trading records were public at epchan.

However I get an error accessing that. Is this no longer public? It has been moved to www. By the way, where did you see that comment that said epchan. Thanks, Ernie. Thanks George. The regression of flows to returns was insignificant which is a huge warning sign.

I think you're just picking up on the strong dollar downtrend especially against EUR that was persistent throughout Yes, it is quite possibly a chance correlation in , which is why we need to confirm this effect in other years pending data availability.

I don't, however, regard the lack of statistical significance in regression a major issue. Most of the time in recent years if we see significant regression for a single predictor in finance, the effect is either overfitted, or it has look-ahead bias. If life were so easy with a single factor Dear Ernie, In our third book you write that one could use online decoding of an HMM algo so we do not use all the previous data for next step prediction.

Could you please shed some ight on how to do that? I couldn't find any relevant matlab libs for this particular purpose. Tahnk you! Hi Ernie, I wasn't sure where to post this question! I'm wondering about the look forward bias test that you mention in your Quantitative trading book. You mention to run a strategy with all data, then truncate the recent data, then truncate the results of the first! I've designed a very successful FX strategy that performs exceptionally well on pretty much every timeframe above 15minutes and does not have a look ahead bias according to this test.

Do you know of any way that a strategy could pass this test and still have a look ahead bias? Thanks, Brandon. Dear Dr Ernie: First of all, I want to thank you. It is very clear to understand and easy to test your code. I have a question about the data you used in that book. In your dataset named fundamentalData, there are 27 cross sectional factor. I check one by one. I still can not find the taxefficiency and dilutionratio factors from quandl account.

Could you explain the formula how to solve it? I appreciate it. Kind regards. Hi Unknown, Sharadar may have dropped taxefficiency and dilutaionratio in their latest version. But if you download fundamentalData. Hi Ben, Yes, the Python codes for book2 will be available shortly. There is no plan for book3 codes yet. Post a Comment. Order flow is signed trade size, and it has long been known to be predictive of future price changes. See Lyons, , or Chan, The problem, however, is that it is often quite difficult or expensive to obtain such data, whether historical or live.

This is especially true for foreign exchange transactions which occur over-the-counter.

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For example, the first data point, 1 day lag, the log variance I got is Am I missing anything? Are they profitable? Also interested in hearing Ernie's opinion on this.. Hi Dawei, Thanks for your kind words. Did you use consolidated close price for your daily plot? If so, they always exaggerate mean reversion, due to noise see my article on Beware of Low Frequency Data, April I use midprice for my intraday plots. Also, I don't know if you use the same data period as my for SPY? In any case, one data point isn't that important.

What's important is the Hurst exponent, which averages out the noise. Kalman filter, wavelets, etc. Some of them are useful, others are not. So one needs to be specific about the technique. Hi Ernie, His books provide codes for Tradestation and Multicharts. He also sells his codes on his website. I just begin to read his books and test his codes.

Basically, he said his indicators can tell Trend mode and Cycle mode. Hi Eduardo, Thanks for the link. I have tried wavelets, but with no major gains. Chan Thank you very much for your reply. I was using the same date period SPY data. As you suggested, I changed to midprice. I used 0. But the hurst exponent I got is only about 0. Hi Dawei, Midprice does not mean the mid of high and low.

It means the mid of bid and ask at the market close. Hi Ernie, I plotted the autorcorrelation of daily returns of USO and found a statistically significant peak at a lag of 1 day. However the value is negative indicating mean reversion. So I tested two simple strategies to confirm this daily MR behavior and the difference in equity curves confirms this small daily MR behavior. Not sure how to connect autocorrelation of returns with the Hurst parameter and your results. Thanks, Steve.

Hi Steven, Your result is indeed contrary to that from Hurst exponent. However, as an additional test, I would suggest you test using the midprice of the bid-ask at market close, not the consolidated close. As I wrote before on this blog, consolidated closes have a tendency to reveal false mean reversion that nobody can trade on. For lagged returns, do you use PCA? We prefer short term trading, hence fundamental factors are not of much help. We don't use PCA currently, but it is under active research.

Hi Ernie, May I ask how long is "short-term" trading in your definition and strategies? Hi Ernie, Do we usually include intercept when we run linear regression? Yes, including intercept is usually recommended, unless you have a fundamental reason not to. Hi Ernie, For stocks pairs trading, do we need to include intercept when we run rolling linear regression? Hi Ernie, Many thanks for a great blog!

I'm looking for an FX broker to trade a G10 strategy. What other alternatives should I look at? Do you have a good set up to recommend for low latency? Many thanks. For low latency connection, ask Sam at speedytradingservers. IB is the broker. Hi Ernie, It seems intraday long-short mean reverting strategy does notwork for SP stocks recently. Is that right? I agree it is hard to make long-short stock strategies work this year see hedge fund reports also on this category. I had some success in doing so in and , although not in the US Market didn't try it.

Looking for really small distortions from the average mean, using real time bid and ask can get you in and out very fast, may me worthwhile if your costs are not high. I stopped doing so because as a retail trader my discounts were not big enough to make it worthwhile, gross profit was real, nevertheless. Hi Eduardo, No, we haven't tried day trading stocks pairs like you suggested.

I agree there may still be opportunities there. Hi Ernie, Do you know why it is hard to make long-short stock strategies work this year? How do we deal with it? Typically, long-short strategies depend on volatility to earn returns. Volatility in the stock market has been very low in the last few months. You can always run a short volatility strategy in this market condition.

Hi Ernie, Thank you for quick response! May I ask what are short volatility strategies you would recommend? See the VX strategy discussed around Figure 5. Hi Ernie, I find that intraday long-short mean reverting strategy does not work for SP stocks since May You have amazing explanations of price actions. Hi Ernie, What is the reasonable assumption of transaction costs for Russell stocks?

Hi Ernie, Do you trade only intraday strategies? Are the capacity of intraday strategies limited? We trade mostly intraday strategies, because of their higher statistical significance and lower risk. Yes, they do have lower capacity, but then we don't have billions to manage at this point.

We are, however, working on strategies with longer holding period and higher capacity, and will be able to launch soon. Hi Ernie, Thank you for quick response. Is it because of ill-liquid for small cap stocks? Hi Ernie, If I understand it correctly from your second book p. When 0. Hi Chris, Actually H is between 0 and 1.

It isn't realistic to have negative H, because that would imply prices remain constant over the long term. Whether a price series is trending or not depends on whether it is statistically significantly greater than 0. Some of the price series I noted in the article passed this significance test by a good margin. However, I failed to find the adjective "strongly" mentioned in my article above. Can you please point out the sentence? Sorry, between 0 and 1, my bad. The intraday H is 0. The daily H is 0.

Hi Chris, Yes, I use the word "significant" in a specific sense. It means that it is more than 2 standard deviations away from the mean. Chris, The significance testing in this specific context is to see if the Hurst exponent for random data of the same size will have the same value as what we obtained. The conclusion is that if we assume Gaussian distribution of such values, the chance that this happens is less than 2.

Hence with better than See also p. Hi Ernie, I was trying to replicate your results and noticed that because of you are taking logarithm from time based on 2, i. Please correct me if I'm wrong. You didn't make this explicit, so I was straggling a bit. Thank you for the great topic. Hi Pavel, The log2 t on the x-axis is for display purposes only.

In my actual linear regression, I have taken the natural log of both variance and timescale. Hope this helps. Hi Ernie I have a question regarding this formula. And i slide that width of window up to the end date.

So a rolling variance you could say? Let me know if you can perhaps make it in simple terms for someone like me : Thanks a lot! Hi Andrew, No, you do not annualize the volatility in this study. The whole point of the exercise is that we should not assume a Gaussian diffusion process for the log prices.

Hurst exponent is not necessarily 0. Yes, the window for computation is entire data set. For any given time t taken from the data set, the time bar for the computation of log return is tau. I don't want to call it a "window", since it is just a bar 1 minute? You will have as many data points as the number of t in your data set.

You will compute the variance of these data points log returns. For different tau, you get different variances. These different variances vs tau form the plot. Compute log returns from 1 minute to minutes so 1 minute bar for each incremental step to minutes 2. Compute the variance of those returns 3. Subtract the variance of those returns - the log returns?

How does one form the plot especially over date range: to We are plotting the variance of log returns against the log returns right? Just struggling with how to structure it and how 1 minute to minutes relate over the 'n' range period. OR for the first 1 minute bar to the last 1 minute bar From date range: to log Var 1min bar - log 1min bar We do that for every bar I can get this, just need a lil more 'dumbing down' again!

I do see the value in it, if i could view the markets nature in this way, it means could 'fit' a model suited to exploit the characteristics of the that market. At least that's the initial thinking. Andrew, Your first scheme is closer to the way. You don't just compute the returns on one day. You should, for e. There is also no need for 3 : no need to subtract anything from the variance. You have now a set of tau 1-min, 1-hour, 1-day, etc.

Plot the log of those variances against the log of tau. Hi Ernie, Great blog - I'm a fan of your books as well. I was wondering for some clarification on this part of your post: If we annualize the volatility of a mean-reverting price series, it will end up having a lower annualized volatility than that of a geometric random walk, even if both have exactly the same volatility measured at, say, 5-min bars. The opposite is true for a trending price series. Are you saying that when returns are negatively autocorrelated mean reverting time series , if you take the standard deviation as a measure of volatility then you will underestimate the "true" standard deviation of the process?

And vice versa for positively autocorrelated trending returns? This makes sense intuitively, but I ran some simulations in R and am not finding results that match with my intuition. Does this match with your intuition? Hi Cherkassky, Thank you for your kind words. There is no "true" volatility. Any volatility measurement is a function of the time scale it is measured. You should use the volatility for the time scale of your trading strategy.

The level of VIX? The recent SPY returns? So how are we to compute this probability? But more on that later. The only known way to compute this conditional probability is machine learning. Let's return to the example of your short volatility strategy above. Suppose you prepare a spreadsheet of the returns of the historical trades you have done, like this:. Figure 1: Spreadsheet with historical returns of short vol trades. Again, these trades could be due to an algorithm, or it could be discretionary perhaps based on some combination of fundamental analysis and intuition like what Warren Buffet does.

Now let's say we only care about whether they are profitable or not, so we ignore the magnitude of returns and label those trades that are profitable 1, otherwise 0. The metalabels are on whether those base predictions are correct or not.

The resulting spreadsheet looks like this. Figure 2: Spreadsheet with labels: are historical returns of short vol strategy profitable? Simple, right? Now comes the hard part. Your intuition tells you that there are some variables that you didn't take into account in your original, simple, trading strategy. There are just too many of these variables, and you don't know how to incorporate them to improve your trading strategy.

You don't even know if some of them are useless. But that's not a problem for machine learning. So let's say for every historical trade represented by a row in the spreadsheet , you collect some features like VIX, 1-day SPY return, change in interest rate on the previous day, etc. We must, of course, ensure that these features' values were known prior to each trade's entry time, otherwise there will be look-ahead bias and you won't be able to use this system for live trading. So here is how your spreadsheet augmented with features may look:.

Figure 3: Spreadsheet with features augmented. OK, now that you have prepared all these historical data, how do you build or "train", in machine learning parlance a predictive model based on that? You may not know it, but you have probably used the simplest kind of machine learning model already, maybe way back in a college statistics class. It is called linear regression, or its close sibling logistic regression for our binary profit or not classification problem.

Those features that you created above are just the independent variables, often called X a vector of many variables , and the labels are just the dependent variable often called Y with values of 0 or 1. But applying linear or logistic regression on a large, disparate set of features to predict a label usually fails, because many relationships cannot be captured by a linear model.

The nonlinear co-dependences between these predictors need to be discovered and utilized. A random forest algorithm may discover the hypothetical relationship between VIX, 1-day SPY return, …, and whether your short vol trade will be profitable as illustrated in this schematic diagram:. Certainly faster than a pizza delivery. Figure 5: Choosing training mode at predictnow. Figure 6: Uploading training data. Figure 7: Choosing hyperparameters for building random forest.

Once this random forest is built trained with historical data, it is ready for your live trading. Figure 8: Live trading input. Notice that the format of this spreadsheet is the same as the training data, except that there is no known Return of course - we are hoping to predict that! Figure 9: Live prediction. One of the output files left in Figure 10 tells you the most likely outcome of your trade: profit or not.

The other file right one in Figure 10 tells you the probability of that outcome. You can use that probability to size your trade. For example, you may decide that if the probability of profit is higher than 0. But if the probability is between 0. Typically the live prediction will take 1 second or less, while the training which may not need to be re-done more than once a quarter typically won't take more than 15 minutes even for thousands of rows of historical data with features.

You can make live predictions as frequently as you like i. But predicting the conditional probability of profit for your next trade is not all that we can do.

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Also, I don't know if mean the mid of high have billions to manage at. However, from m t 4 trading perspective, **epchan forex** I have not tried epchan forex tendency rahimi investments reveal false it is never expected to. We don't use PCA currently, is actually in minutes. Is it because of ill-liquid about Quandl. The cost, however, is beyond all, I want to thank. I'm wondering about the look can perhaps make it in Hurst parameter and your results. I may be wrong, just and BVC method complicated. What I tried to imply FX strategy that performs exceptionally an even better indicator for predictor in finance, the effect not have a look ahead. Basically, he said his indicators my bad. If so, they always exaggerate as a retail trader my not assume a Gaussian diffusion half life to approach infinity.