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 Beginner's Intro
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predicted
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Theory of Neural Networks (J. Moody)
Typical published example (from the literature): | |
A novel FOREX prediction methodology based on fundamental data |
Summary of operation:
- The trader, wishing to quantify the relationship among a group of stock or share prices, and/or indices, enters the tickers in capital letters, separated by commas.
- The needed historical and real time share price quotes and volumes are looked up and compared automatically.
- The neural network searches for a nonlinear mathematical relationship (pattern) relating the prices and volumes to the ticker of interest, while the user participates by controlling a sensitivity (also called 'momentum') adjustment
- When sensitivity is then set to zero, graphs show two years of correct and rigorous backtesting. through which the user may visually assess whether the relationship is valid throughout historical time.
- The relationship is extended into the future to make a forecast, by the number of days the user has set on the slider during training.
- There is no buy/sell indicator: the reliability of the forecast depends on the user's visual verification of the match between the two graphs obtained during backtesting, and the his estimation of the likelihood that the mathematical relationship which has been found will continue to hold in the future.
User instructions
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1. Think of a list of tickers that are likely to be mathematically
related, over time. This is the hardest step -- mathematically related does not mean that they behave similarly.
2. Locate the label which says 'Related Group of Tickers.' Beneath this, delete the sample list of tickers which may be there, and enter the names of the tickers you are interested in analyzing, in capital letters, and separated by commas. 3. Choose a data source from the File: button at the top of the program. The simplest possibility is 'Load from Internet.' The program knows how to search the standard list of public domain websites including Google, Yahoo and MSN and place a plain text file called Internet.txt in the GoldenGem folder, which will have columns for Date, Ticker, Open, High, Low, Close, and Volume, with entries separated by commas. The default settings in StockDownloader are set to load the same list of stocks which you entered in GoldenGem, up to the most recent close price. If you are happy with this selection, press the 'Load' button on StockDownloader. (The StockDownloader is just a beginners' tool; it is expected that you will later graduate from technical analysis to fundamental analysis finding your own data from other websites; click here to find out how to do this.)
4. Use the up and down arrows on your keyboard to choose which of the the various graphs you wish to see (or you can use the drop-down menu button, near the letters TK to the right of the screen). |
Click to continue
| 5. You will see three coloured traces on each graph. The red
trace is the graph of the selected share price throughout two years of
historical time. The green graph shows the
neural net's use of artificial intelligence to predict the red graph,
throughout historical time, simultaneously combining all loaded volume
and price information of all the shares. There is also
a blue graph, which shows where the green graph would go if it were
predicting perfectly. 6. Increase the sensitivity and wait until the green graph matches the blue graph, then decrease the sensitivity to zero (the bottom of the slider). This is best done in stages, over many iterations, as you are removing the training input. If the green and blue graphs match well when sensitivity is zero, this means the green curve has learned how to predict the advance copy of the red curve throughout historical time, by looking only at effects as many days in the past as you have selected on the Days slider. The green curve does not therefore need to finish at the 'Loaded Until' date, but can continue the calculation for an equal number of days yet to come. You see this in the first screenshot as the part of the green curve that extends three weeks past the verticical blue line. 7. The numerical prediction for the future increase or decrease will also be shown at the upper right of the display. A text display will say, for instance: Predicted change over the next 15 business days: +2.35. |
For more detailed instructions go to the FAQ page (click here)
For technical specifications to click here
| Other data sources: Besides using StockDownloader, there
are other options for obtaining data. For three beginning examples, you can
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| *choose 'Load from File,' to load data which you may have downloaded previously, such as the standard and poor 500 file sp500hst.txt which uses the same standard file conventions as GoldenGem, and can be obtained as a file called full_set.zip from biz.swcp.com/stocks/ | |
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| *download a collection of .csv files (some of stock market data and some of Forex data) from for example www.forexrate.co.uk/forexhistoricaldata.php. The csv data export on that site works by scrolling with the up and down arrow keys. Select 500 data points, 1 day, and split by comma. Change the vol number in GoldenGem to column 4, enter the ticker names you wish into GoldenGem, and choose Import from a folder. Then browse to whatever folder contains the .csv files. | |
| or | |
| *paste or type in a text file of your own using Wordpad, as explained in the link below | |
| or | |
| *download a file of 2 years or so of daily data from the Bank of England Statistical database http://www.bankofengland.co.uk/statistics/index.htm Choose columnular (with titles if you wish). This creates a file called results.csv. Set the file position numbers tick, date close to 2,1,3 and leave the vol window blank. Type in the subset of ticker names you want to start with. Choose 'Browse for a new file' from GoldenGem's File button. Choose 'comma separated (csv) files' at the bottom of the file selecting box and select 'results.csv,' the file you've just downloaded from Bank of England. | |
Availability:
Try it online!
If you have a Windows XP operating system, with Internet Explorer or Mozilla you may find it is not necessary to install the program at all; you're invited to click here to try it online. For Internet Explorer choose 'run' instead of 'save'. For Mozilla choose 'save' then 'open.'
The link above is a small 504 K file called GoldenGem Viewer. Choose to 'load tickers from the internet' from the File: menu, then use the menu to the right of the screen to change between graphs. If it is the first time you've trained a neural network, think of it as a sort of video game, where the goal is to make both indicator lights remain green.
Full installation:
1. Local download Link (same as on the home page)| 2. The Download.com site, which delivers the same file, verifies that software which they offer is free of adware, spyware, or viruses, and that it correctly installs and uninstalls. It does not matter whether you download from the local link or from Download.com. |
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Safety information:
1. Mcafee site advisor check of goldengem.co.uk and setupv.exe.2. Digital signature. The 'properties' menu for digitally signed files always includes an extra Digital Signatures tab to ensure that a file has never been altered. It allows you to verify the following information:
| File Name: setupv.exe | File Size: 2.05 MB | Signed by: Dr. J Moody | Date Signed: 31 October, 2011 |
| Counter-signature: Comodo | Certificate: UTN | Time signed: 22:24:42 |
Operating systems supported by version 2.4
Windows 98, 2000, NT, XP Vista and Windows 7.Advantages:
- Benchmark predictions of abstract mathematical functions, such as shown on this site, have been extensively verified.
- The technical specifications are those agreed to be most effective in stock market prediction.
- The algorithm has been widely used for many years in finance, trading and investment, portfolio management. It is established as a reliable and valuable calculation.
- The algorithm is the only reliable way in which it is possible to
simultaneously consider the combined effects of a number prices and
volumes.
Test your copy of GoldenGem
We've made a file of tickers named x, y, z,w
containing
10 sin(i/10), 10 cos(i/10),
10 cos2(i/10)sin(i/10)+10 cos(i/10), and
10 sin 2(i/10)-10 cos(i/10)-10.
for i=1 to 400. Right click proof2.txt and choose 'save target.' Set the ticker names x,y,z,w and file position numbers 1 (blank) 2 (blank) as in the screenshot below. Press the File: button, select 'Browse for new file' and locate the file proof2.txt which you have just downloaded. Then have fun!
The fact we've used repeating functions is just so the viewer can recognize whether the predictions are correct. The program does not use the fact that they are repeating functions in any way. Note that since the DAYS slider is set to 21, the predicted change is given 15 data points ahead, as the DAYS slider is in terms of actual days not including week-ends, so it will state that the prediction is for 15 business days in the future.
for i=1 to 400. Right click proof2.txt and choose 'save target.' Set the ticker names x,y,z,w and file position numbers 1 (blank) 2 (blank) as in the screenshot below. Press the File: button, select 'Browse for new file' and locate the file proof2.txt which you have just downloaded. Then have fun!
The fact we've used repeating functions is just so the viewer can recognize whether the predictions are correct. The program does not use the fact that they are repeating functions in any way. Note that since the DAYS slider is set to 21, the predicted change is given 15 data points ahead, as the DAYS slider is in terms of actual days not including week-ends, so it will state that the prediction is for 15 business days in the future.
Registration is required after one month of use
If you enjoy the program and wish to register it after one month of use a permanent registration key will cost 75 US dollars; at the end of one month of use a registration key for GoldenGem can be obtained through a paypal order by using the button below. Registration entitles you to unlimited use of the program and all future upgrades.Important: Don't close the last Paypal page without clicking the link which says "Click here for your registration key." If you close the last paypal page without clicking the link there will a 24 hour delay while staff email your registration key.
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Contact Us
Postal Address: GoldenGem Neural Networks, 12 Armorial Road, Coventry CV3 6GJ, England
Telephone (if dialled from USA): 01144-2476-523490
Software author (if dialled from USA): 01144-2476-417946
Support: GoldenGemNetwork@gmail.com
Beginner's Introduction
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Currently, the site is visited by programmers, serious investors, students (mostly Masters and PhD), some emeritus prof.'s and hobbyists, most of whom are already involved in neural networks. We would like to change that, and we are working on making the site and the program more accessible. Questions and AnswersWhat are the main types of financial analysis? There are two main types. Technical Analysis attempts to extrapolate a price using only earlier values of that same price. Only a few papers show a statistically significant advantage over random trading. The second type is Fundamental Analysis, where data from financial statements, interest rates, volumes, competitors' prices, prices of raw materials or other variables known to affect the target prices are used. As explained by Wikipedia (see the General Information link on the left), neural networks act as a 'bridge' between Technical Analysis and the more highly regarded Fundamental Analysis. Future values of prices are approximated, not by nonlinear extrapolation of earlier values, but by hypothesizing and testing actual causal connections. It is possible to load a set of prices and volumes of the most well-known shares, bonds, and indices by pressing one button. But unless one has the skill to find a meaningful relationship among these variables, StockDownloader is really only for beginners. A competent user will graduate to using other types of data, available on the internet and also accessible by GoldenGem with a bit more work; the instructions link on the left leads to a series of links explaining further types of data that can be imported, and we're willing to modify the program to make it compatible with new formats as they come up. Developing this implementation over time we have needed to confront subtle questions before the algorithm began to work well, as it now does. The Verification link on the left shows GoldenGem predicting abstract mathematical functions. The program does not use the fact that the functions are repeating; only today's values of the input variables are used in making the last day of prediction. In cases when earlier values than the last known value may have an effect you should also include `stochastics' as input variables. The program does not take into account 'oscillations' such as described in the Elliot wave 'theory,' a theory which we do not subscribe to; however, note that the program is able to predict functions that oscillate. Finally, the program is able to predict any one of the functions knowing the others, but there is no preferred process of extrapolation that should would work for a single function treated by itself. How reliable is a prediction that this program gives me? We have added a pair of indicator lights to help answer this in each case. The first indicator light refers to a number r during backtesting, which is defined to be the minimum of the correlation coefficient of predicted versus actual daily changes, on the one hand, and the correlation coefficient times the ratio (actual variance)/(predicted variance), on the other hand. To give some idea of the meaning of this: if a person were to buy or short, every day during backtesting, in proportion to its predicted percentage change, assuming things are normally distributed, the percentage profit over n days of trading during that time in the past would have been
where V is the maximum of actual and predicted annual volatility expressed in percentage points. This taking of V to be the maximum of actual and predicted volatility seems contrived, but it is exactly what one wants. If r and V were defined using only the actual volatility, then a strategy of relying upon a posteriori information would exist to attain deceptively good returns during backtesting which do not really result from any prediction: a sluggish response, in which the green curve stays near the 2 year average, would correspond to a strategy of predicting always a sudden return to the 2 year average, which during backtesting includes knowledge of future days and would unfairly reward the correlation coefficient alone. Whereas if r and V were defined using only the predicted volatility, there would be no intrinsic relation between r and the actual percentage gain: a large r value could arise from a prediction with very low variance. The value of r as we have defined it rules out both these problems, and appears to correspond with what looks like intuitively good backtesting. The first light is yellow when the r value is larger than 0.39 and green when it is larger than 0.6. The second light goes from red to yellow to green as the training input is removed. You will need to try different combinations of input variables before you will be able to make both lights remain green at the same time. If the lights cannot be made to remain green, the answer to your question is, the prediction is meaningless. If the lights do remain green, then that means a relationship has been found which has been able to make succesful predictions during the backtesting interval. When both lights have remained green, does this imply the prediction can be trusted? Not yet. Even taking account the variance ratio as we have, the formulation in terms of profit shows that this number could be high enough to set the green light, just because some of the hypothetical trades were extremely profitable, others not at all. You also need to to actually look at the behaviour of the prediction line, the part of the green line extending into the future, past the red line, throughout backtesting, and see qualitatively how consistenly it is correct. When sensitivity is set to zero there is no training input, and the green graph is calculated only using data values of all variables from the time of the earlier red graph, and any prediction you see therefore shows a real mathematical relationship during backtesting. Finally, you still are not quite done. Even when you have assessed both statistically and visually that the predictions throughout backtesting are good, to be really sure the variables you are looking at are related, you should set 'today's date' in StockDownloader to a time in the past, or otherwise load data only up various times in the past, and train the net to predict a range of values which you actually already know. This is a 'validation data set,' and the next version of GoldenGem will make this last stage of validation easier. There do exist relationships between variables which are known to affect prices, the ones which are well-known can't be exploited unless you have knowledge of the input variables advance of the trading public. It is not true that all existing relationships are well-known. Insider trading is perfectly legal if you exploit public domain information through your own intelligence. What will happen the first time I try it? A good training strategy is to start with a high sensitivity, and to bring it down in stages. Assuming the variables actually were expected to be related, and your training strategy was correct, you are likely to end up with the first light turning red, signifying inadequate correlation, by the time the second light turns green. This is usually for one of three reasons: 1. If you see vertical green spikes, and a message 'Press the Reset button,' then you have traumatized the net. Like a human or animal, it will take a very long time to recover. Similar to a good nights' sleep might do for an animal, the Reset button gives a fresh start, and all is forgiven, but it will need to be trained again from the beginning. 2. If the green line is flat, this is because it was trained inadequately. Raise the sensitivity slider again and wait a while before bringing it down (ideally in stages). 3. If the green line looks just like the red line, but shifted to the right by the amount on the Days slider, you are seeing a situation where the expected future value is always nothing but the last known value. If all graphs are like that, congratulations, you have found a 'Markovian' set of shares: interesting but with no opportunity for arbitrage, assuming the neural network has found the best possible solution. ConclusionFinancial analysis is something you do, not something you buy. A neural network requires involvement by the user. You have to choose what data you think is relevant, you have to learn how to train the net, and it is up to you to evaluate the backtesting. The most important thing to remember is that although our display shows only two graphs at a time (actual and predicted), the predicted graph is generated by taking account of the mathematical relations among the prices and volumes of all loaded variables simultaneously and so the choice of ungraphed tickers affects the quality of the match between the two graphs you are observing. It is your responsibility to decide whether you are discovering and exploiting a valid and rational mathematical relationship which others haven't yet thought of. Think of the famous story of the investor who profited decades ago, during the beginning of the urban legend of a mouse in a KFC meal. He counted the change in the number of people attending his local KFC each day, and decided there had been no decrease. There is a valid, simple, and meaningful relation between the number of people he observed, the current share price and the future share price, which he used intuitively. If he had wanted to be more precise he could have used a neural network. It is not guesswork, it is not a fishing or `data mining' expedition. You have to already know what you are doing and why. |
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(Never made a website before? Don't worry, just paste the code into Notepad, add whatever text you like, and save as a file called favourite.html on your desktop. You have made your first website. You can email it to colleagues or put upload it to a server.)