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Support Vector Machine Learning Tool

This is an easy-to-use tool for implementing Support Vector Machine Learning in your Expert Advisors, Indicators and other MetaTrader 5 projects.

Until now, the use of support vector machine classification has been limited only by advanced coders via external java and c++ dll libraries. This tool has been developed using only the standard MetaTrader 5 tools and provides advanced support vector machine functionality using a very simple interface.

Please note: this product is not an Expert Advisor or Indicator. This is a library that allows users to implement support vector machine classification in their own Expert Advisors and Indicators.

What is a Support Vector Machine?

Support vector machines (svm) are a form of machine learning that use a supervised learning algorithm to analyze data and recognize patterns to be used for classification. They are used most prominently in fields such as bioinformatics and mathematics, however this library has been specifically developed with the intention to use support vector machine learning to analyze historical price data and extract patterns that can be used to generate signals.

If you want to find out more about the support vector machines' mechanics and how they work, I suggest you start with the Wikipedia page https://en.wikipedia.org/wiki/Support_vector_machine. The article provides good overview and further links if you are interested in looking into it further.

How the Library Works:

The basic process for any support vector machine is as follows:

  • Gather historical market price and indicator data;
  • Use historical data to generate a set of training inputs and outputs;
  • Use these historical inputs and outputs to train the support vector machine;
  • Use the trained support vector machine to analyze current market price and indicator data to signal new trades.

A support vector machine is basically an input/output machine. The user passes input(s) to the machine and it produces an output of either true or false. If the support vector machine has not yet been trained, it will usually give only a random output for any given input. To have the support vector machine produce a useful output, it must first be trained.

The training of a support vector machine is done by passing in a set of inputs with a set of corresponding desired outputs. The support vector machine algorithm will then use this combined dataset to extract patterns. In the case of this tool, the inputs used are indicators (inputs can be any combination of standard or custom indicators selected by the user) and the outputs are either true or false (corresponding to whether a new trade should be opened).

Once the indicators (to be used as inputs) have been selected by the user along with the parameters for determining outputs, the tool will generate a set of inputs and outputs to be used for training the support vector machine. Once this is done, training can be commenced. Once the support vector machine has been successfully trained, it can be used to take current indicator values as inputs and signal the Expert Advisor to either make a new trade, or not.

Advanced Users: additional functions have been included to allow users to manually create and set your training inputs and outputs. This can be used for more complex applications such as signalling when to exit a trade or for money management. To do this, see details below on the setInputs() and setOutputs() functions.

A variety of functions have been included for both basic and advanced users. These are outlined below:

Function

Description

#import "svMachineTool.ex5"

<function signatures>

#import

This statement is called to at the top of your Expert Advisor/indicator project to import the svMachineTool functions. If you don’t call this, you won’t be able to use any of the svMachineTool functions

int initSVMachine(void);

This function initializes a new support vector machine and returns a function handle that can be used for further computation.

bool setParameters(int handle, ENUM_OPTION option,double value)

This method passes your parameters to the new support vector machine that has been created. Parameters need to be passed one at a time using this method. If no parameters are passed to the object, then the parameters will assume their default values (for the default values refer to the parameter table).

bool setIndicatorHandles(int handle, int& indicatorHandles[],int offset, int N)

This function allows you to pass in the handles of initialized indicators to the support vector machine. This is done by passing and int array with the handles. The offset value corresponds to the last candle (on the current chart timeframe) that you wish to use for your training data and the value for N sets the number of training points.

bool genInputs(int handle, int N)

This function generates the support vector machine inputs to be used for training. To generate the inputs, it uses the indicators handles passed in via the previous setIndicatorHandles function and generates N training points starting from the current time and generating indicator values using the current chart timeframe. This function will either return true or false based on whether the inputs are successfully generated. Any errors will be printed to the error log.

bool setInputs(int handle, double& Inputs[], int nInputs)

This function can be used to manually set the input values used for training your support vector machine. The inputs must be passed in as an array of double values in this format.

Inputs[] = | A1 | B1 | C1 | A2 | B2 | C2 | A3 | B3 | C3 |

nInputs denotes the number of inputs per training point. In the example above, a single training point consists of an A B & C value, therefore the value of nInputs would be 3. The function returns either true or false depending on whether the Inputs are set successfully.

bool genOutputs(int handle, ENUM_TRADE trade, int StopLoss, int TakeProfit, double duration)

This function can be used to automatically generate your output array for training your support vector machine. The output array is generated by looking at the historical minute candle data and taking ‘hypothetical’ trades based on the variable passed to it and determines whether or not each of these trades would have been successful.

ENUM_TRADE = This variable sets the type of ‘hypothetical’ trade you want your svm to train for. This can be either BUY or SELL

StopLoss = sets the stop loss of ‘hypothetical’ in pips

TakeProfit  = sets the take profit of ‘hypothetical’ in pips

Duration = sets a maximum duration of the ‘hypothetical’ trade [in hours] to be deemed successful. (i.e. if duration is set to 2, then only ‘hypothetical’ trades that close within 2hrs will be considered successful in the training outputs).

This function will either return true or false based on whether the inputs are successfully generated. Any errors will be printed to the error log.

bool setOutputs(int handle, bool& Outputs)

This function can be used to manually set the Output values used for training your svm. The Outputs must be passed in as an array of boolean values. The function returns either true or false depending on whether the Inputs are successfully set. Any errors will be printed to the error log.

bool training(int handle)

This function is used to initialize training of your support vector machine using the initialized training inputs and outputs. This function will generally take the longest to execute. The support vector machine must be trained before you can use it to classify new inputs.

bool classify(void)

bool classify(int offset)

bool classify(double& Input)

The classify function uses the trained support vector machine to assess new inputs and return a value of true or false.

Void = If no variables are passed to the classify method, then inputs will be generated using the latest indicator data.

Offset = used to generate inputs from old data. The offset specifies how many bars offset from the current bar (using the current time frame).

Input = used to manually set the input to classify (note: this classify method must be used if input data has been manually set using setInputs())

void  deinitSVMachine(void)

This function is intended to be called at the end of your Expert Advisor/Indicator. This function deletes all of the unused variables to avoid taking up space.

void setIndicatorHandles(int handle,int& indicatorHandles[],int& Offsets[], int startBar,int N)

This function works in a similar way to the original setIndicatorHandles() function however it also has the ability to pass specific offsets corresponding to each of the indicator handles passed to it. As with the previous version the indicatorHandles[]  array stores the indicator handles for your selected inputs while the Offsets[] array stores the corresponding indicator offset (i.e. offset value of 0 will use the current bar when generating inputs, whereas an offset value of 1 will use the previous bar…and so on). For example, assume the following scenario:

indicatorHandles[5] = [ 1 | 2 | 3 | 3 | 3 ] = {1,2,3,3,3};
Offsets[5]             = [ 0 | 0 | 0 | 1 | 2 ] = {0,0,0,1,2};

In this scenario, input 1 and 2 are calculated using the current bar of two individual indicators, whereas inputs 3, 4 & 5 are calculated based on the past 3 bars of the same indicators. This function can be used to pass in a snapshot of an indicator to the support vector machine (i.e. a couple of bars).

The remaining variables required for this function are the startBar value and the N value. startBar corresponds to the last candle (on the current chart timeframe) that you wish to use for your training data and the value for N sets the number of training points. For example, a startBar value of 4 and an N value of 6 will tell the support vector machine to only bars 4 to 9 when generating training inputs and outputs.

bool getTrainingData(int handle, double& Inputs[], bool& Outputs[])

This function allows users to access the training data that is generated when calling the genInputs() and the genOutputs() function. The first variable required for this function is the handle of the support vector machine. The following two variables required are an int-array and a double-array. When called, the function will populate these two arrays with the training data generated for the support vector machine. Note that if this function is called before the genInputs() or the genOutputs() functions are called, it will return arrays of zeros length.

Certain parameters of your support vector machine can be set using the setParameters() function call. These parameters are explained below:

Option Field

Default Value

Description

OP_MEMORY

1000

Training the support vector machine can consume a significant amount of memory. This option sets the maximum memory footprint you want the support vector machine to take. The value given is measured in MB. If a memory value of 1000 MB is set, then the training algorithm will manage its memory to keep its foot print below this level. This should be considered particularly if you choose to perform back testing across multiple cores. For example, if I have a quad core computer (corresponding to 4 local testing agents) and I have 8 GB RAM, I will generally set my memory value to about 1250 MB. This will mean that when training is being executed in parallel across all local agents, only a maximum of 5000 MB (4 x 1250 MB) will be used leaving 3000 MB for the operating system and other programs without causing problems.

OP_MAXCYCLES

1000000

This will limit the maximum number of training cycles that will occur. The reason for this is to avoid the scenario where training never stops. This can occasionally happen because it is trying to achieve an impossible solution. Unless you have a specific reason, I recommend you don’t manually change this value.

OP_ TOLERANCE

0.1

This value sets the maximum error you are willing to accept from the final support vector machine. The input for this is a percentage (i.e. 0.1 is 10% error, 0.15 is 15% error). If you are finding that your training doesn’t converge on a solution, I recommend you increase the acceptable error tolerance value.


Example: How to Use the Support Vector Machine Tool to Signal Trades

An example of Expert Advisor (“svmTrader”) has been written to show a typical use of the support vector machine learning tool. You can download it for free from Code Base.

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Alarmist
Alarmist
Edgar Akhmadeev
There is a separate free demo library AlarmistDemo   with fixed messages. The demo version of this library would not work, because send functions do not work in the tester. Alarmist library for expert developers is a universal engine for sending messages to the  Pushover  and  Telegram   programs or by the functions  SendNotification() ,  SendMail() . The possibility of using buttons in  Telegram  and sending to other messengers ( Twitter ,  Viber ) and several specialized programs is being cons
30 USD
Goliath Mt5
Goliath Mt5
Nicolokondwani Biscaldi
Goliath MT5 - scalper fully automated Expert Advisor for medium-volatile forex markets P roperties: The Library trades 10 currency pairs (USDCHF, EURCHF, EURGBP, AUDUSD, USDCAD, GBPUSD, EURUSD, NZDUSD, CADCHF, EURAUD, EURCAD, AUDJPY) The Library does not use martingale The Library sets a fixed stop loss and take profit for all orders The Library only trades a user input volume The Library can be installed on any currency pair and any timeframe Recommendations: Before using on a real account,
819.99 USD
Wsslib
Wsslib
Soewono Effendi
You can now easily provide WEBSOCKET connection to javascript to your MT terminal. An easy to use websocket server library for MQL5. It supports: ws:// and wss:// (secure websocket) text and binary data automatic ping-pong handling on protocol level (keep-alive protocol hand shake) automatic fragmented message handling on protocol level (large data transfer enabled) ================================================== ATTENTION:  PLEASE CONTACT SELLER BEFORE PURCHASE. Due to the nature of t
50 USD
Sememas
Sememas
Joao Carlos Almeida Tolovi
Lorem ipsum dictumst tortor ut arcu convallis habitasse, tristique dapibus mattis commodo a pellentesque. turpis torquent platea malesuada nisi non cras auctor faucibus aliquam, aliquet congue etiam tempus primis aenean commodo mi, morbi amet inceptos eros ullamcorper dapibus hendrerit tristique. ullamcorper venenatis sociosqu justo ligula porttitor lacinia ipsum id hendrerit, imperdiet adipiscing dolor lectus pharetra phasellus sociosqu libero tempor, quisque primis etiam mattis malesuada vulp
30 USD
Binance Library
Binance Library
Hadil Mutaqin SE
The library is used to develop automatic trading on Binance spot market from MT5 platform. Support all order types: Market, Limit and Stop Limit Automatically display the chart on the screen Usage: - Move BinanceLib.ex5 from folder \MQL5\Scripts\Market to MQL5\Libraries - Copy Binance.mqh header file from GitHub to folder \MQL5\Include - Copy  BinanceEA-Sample.mq5 from GitHub to folder \MQL5\Experts     https://github.com/LazellnetLabs/BinanceMT5/raw/main/BinanceHeaderFile.zip - Example how
500 USD
Gold plucking machine
Gold plucking machine
Yan Li Wu
Gold plucking machine   Gold plucking machine is an Expert Advisor designed specifically for trading gold. The operation is based on opening orders using the Fast and Slow lines indicator, thus the EA works according to the "Trend Follow" strategy, which means following the trend. Use grid strategy to place orders without stop loss operation, so please make sure the account has sufficient funds. magic number      -  is a special number that the EA assigns to its orders. Lot Multiplier        -
49 USD
Gold plucking machine S
Gold plucking machine S
Yan Li Wu
Gold plucking machine S   Gold plucking machine  S Gold plucking machine S   is an Expert Advisor designed specifically for trading gold. The operation is based on opening orders using the Fast and Slow lines indicator, thus the EA works according to the "Trend Follow" strategy, which means following the trend. Use grid strategy to place orders without stop loss operation, so please make sure the account has sufficient funds. magic number        -  is a special number that the EA assigns to its
49 USD
MT5QuantLib
MT5QuantLib
Chao Chen Li
软件使用之前,请采用以下方式导入函数 #ifdef __MQL4__ #import "vx_mtquant.ex4" #endif #ifdef __MQL5__ #import "vx_mtquant.ex5" #endif // 交易函数 ulong BuyScaleRatioStep( double ratio, int step_pips, double volume, int sl_pips, int tp_pips, ulong magic, string symbol, string comment); ulong SellScaleRatioStep( double ratio, int step_pips, double volume, int sl_pips, int tp_pips, ulong magic, string symbol, string comment); ulong BuyScaleStep( int step_pips, double volume, int sl_pips, int tp_pips, ulong magic, string
299 USD
Binance Futures Library
Binance Futures Library
Hadil Mutaqin SE
The library is used to develop automatic trading on Binance futures market from MT5 platform. Support all order types: Market, Limit and Stop Limit Automatically display the chart on the screen Usage: - Move BinanceFuturesLib.ex5 from folder \MQL5\Scripts\Market to MQL5\Libraries - Copy BinanceFutures.mqh header file  from GitHub to folder \MQL5\Include - Copy  BinanceFuturesEA-Sample.mq5 from GitHub to folder \MQL5\Experts     https://github.com/LazellnetLabs/BinanceFuturesMT5/raw/main/Binanc
500 USD
Support Vector Machine Learning Tool
Support Vector Machine Learning Tool
Josh Readhead
This is an easy-to-use tool for implementing Support Vector Machine Learning in your Expert Advisors, Indicators and other MetaTrader 5 projects. Until now, the use of support vector machine classification has been limited only by advanced coders via external java and c++ dll libraries. This tool has been developed using only the standard MetaTrader 5 tools and provides advanced support vector machine functionality using a very simple interface. Please note: this product is not an Expert Advisor
20 USD
MT5 Logger
MT5 Logger
Zoltan Meszaros
Description The CLogger class provides a simple but sophisticated logging utility that you can use to output messages. The messages have associated levels, such as INFO or ERROR that indicate their importance. You can then give the CLogger a level, and only messages at this level or higher will be saved. The levels are: DEBUG : Low-level information for developers. INFO : Generic (useful) information about system operation. WARNING : A warning. ERROR : A handleable error condition. Public method
10 USD
Trades Manager MT5
Trades Manager MT5
Omar Alkassar
The library is dedicated to help manage your trades, calculate lot, trailing, partial close and other functions. Lot Calculation Mode 0: Fixed Lot. Mode 1: Martingale Lot (1,3,5,8,13) you can use it in different way calculate when loss=1 ,when profit=0. Mode 2: Multiplier Lot (1,2,4,8,16) you can use it in different way calculate when loss=1 ,when profit=0. Mode 3: Plus Lot (1,2,3,4,5) you can use it in different way calculate when loss=1 ,when profit=0. Mode 4: SL/Risk Lot calculate based on
249 USD
MetaCOT 2 CFTC ToolBox MT5
MetaCOT 2 CFTC ToolBox MT5
Vasiliy Sokolov
MetaCOT 2 CFTC ToolBox is a special library that provides access to CFTC (U.S. Commodity Futures Trading Commission) reports straight from the MetaTrader terminal. The library includes all indicators that are based on these reports. With this library you do not need to purchase each MetaCOT indicator separately. Instead, you can obtain a single set of all 34 indicators including additional indicators that are not available as separate versions. The library supports all types of reports, and prov
95 USD
WalkForwardOptimizer MT5
WalkForwardOptimizer MT5
Stanislav Korotky
WalkForwardOptimizer library allows you to perform rolling and cluster walk-forward optimization of expert advisers (EA) in MetaTrader 5. To use the library include its header file WalkForwardOptimizer.mqh into your EA source code, add call provided functions as appropriate. Once the library is embedded into EA, you may start optimization according to the procedure described in the User guide . When it's finished, intermediate results are saved into a CSV file and some special global variables.
85 USD
WalkForwardLight MT5
WalkForwardLight MT5
Stanislav Korotky
This is a simplified and effective version of the library for walk forward analysis of trading experts. It collects data about the expert's trade during the optimization process in the MetaTrader tester and stores them in intermediate files in the "MQL5\Files" directory. Then it uses these files to automatically build a cluster walk forward report and rolling walk forward reports that refine it (all of them in one HTML file). Using the WalkForwardBuilder MT5 auxiliary script allows building othe
85 USD
BitMEX Trading API
BitMEX Trading API
Romeu Bertho
Cryptocurrency analysis has never been easier with Crypto Charts for MetaTrader 5. Now, trading on BitMEX has never been easier with BitMEX Trading API for MetaTrader 5. BitMEX Trading API library was built to be as easy to use as possible. Just include the library into your Expert Advisor or Script, call the corresponding methods and start trading! Features Trade on BitMEX and BitMEX Testnet. Build and automate your strategies. Concern more with the trading strategy logic and less with the c
500 USD
Painel De Expert Com Teclado
Painel De Expert Com Teclado
Roberto Spadim
Teclado trader, é uma BIBLIOTECA que você pode chamar no OnChartEvent para abrir posição de compra/venda/zerar, os botões padrões são: V = venda C = compra Z = zerar posições a mercado S = zerar posições opostas e depois a mercado X = zerar posições opostas Além da função de teclado, é possível mostrar os estados do ExpertAdvisor usando o MagicId, com informação de: lucro mensal, semanal, diario, e posição aberta, para isto use o OnTick, ou qualquer outro evento (OnTimer / OnTrade / OnBookEv
30 USD
Easy websocket library for mql5
Easy websocket library for mql5
Soewono Effendi
An easy to use websocket library for MQL5. It supports: ws:// and wss:// (secure websocket) text and binary data automatic ping-pong handling on protocol level (keep-alive protocol hand shake) automatic fragmented message handling on protocol level (large data transfer enabled) ================================================== ATTENTION:  PLEASE CONTACT SELLER BEFORE PURCHASE. Due to the nature of this kind of library,  FULL FEATURE DEMO VERSION CAN BE PROVIDED ON REQUEST ==============
30 USD
Alarmist
Alarmist
Edgar Akhmadeev
There is a separate free demo library AlarmistDemo   with fixed messages. The demo version of this library would not work, because send functions do not work in the tester. Alarmist library for expert developers is a universal engine for sending messages to the  Pushover  and  Telegram   programs or by the functions  SendNotification() ,  SendMail() . The possibility of using buttons in  Telegram  and sending to other messengers ( Twitter ,  Viber ) and several specialized programs is being cons
30 USD
Goliath Mt5
Goliath Mt5
Nicolokondwani Biscaldi
Goliath MT5 - scalper fully automated Expert Advisor for medium-volatile forex markets P roperties: The Library trades 10 currency pairs (USDCHF, EURCHF, EURGBP, AUDUSD, USDCAD, GBPUSD, EURUSD, NZDUSD, CADCHF, EURAUD, EURCAD, AUDJPY) The Library does not use martingale The Library sets a fixed stop loss and take profit for all orders The Library only trades a user input volume The Library can be installed on any currency pair and any timeframe Recommendations: Before using on a real account,
819.99 USD
Wsslib
Wsslib
Soewono Effendi
You can now easily provide WEBSOCKET connection to javascript to your MT terminal. An easy to use websocket server library for MQL5. It supports: ws:// and wss:// (secure websocket) text and binary data automatic ping-pong handling on protocol level (keep-alive protocol hand shake) automatic fragmented message handling on protocol level (large data transfer enabled) ================================================== ATTENTION:  PLEASE CONTACT SELLER BEFORE PURCHASE. Due to the nature of t
50 USD
Sememas
Sememas
Joao Carlos Almeida Tolovi
Lorem ipsum dictumst tortor ut arcu convallis habitasse, tristique dapibus mattis commodo a pellentesque. turpis torquent platea malesuada nisi non cras auctor faucibus aliquam, aliquet congue etiam tempus primis aenean commodo mi, morbi amet inceptos eros ullamcorper dapibus hendrerit tristique. ullamcorper venenatis sociosqu justo ligula porttitor lacinia ipsum id hendrerit, imperdiet adipiscing dolor lectus pharetra phasellus sociosqu libero tempor, quisque primis etiam mattis malesuada vulp
30 USD
Binance Library
Binance Library
Hadil Mutaqin SE
The library is used to develop automatic trading on Binance spot market from MT5 platform. Support all order types: Market, Limit and Stop Limit Automatically display the chart on the screen Usage: - Move BinanceLib.ex5 from folder \MQL5\Scripts\Market to MQL5\Libraries - Copy Binance.mqh header file from GitHub to folder \MQL5\Include - Copy  BinanceEA-Sample.mq5 from GitHub to folder \MQL5\Experts     https://github.com/LazellnetLabs/BinanceMT5/raw/main/BinanceHeaderFile.zip - Example how
500 USD
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rod777
200
rod777 2020.08.24 12:01 
 

I have purchased suport vector machine learning tool and activated it, but whem I compile the indicator shows error.

JasperFu
20
JasperFu 2012.12.18 12:38 
 

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