Mulai

Machinelearning

Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto) Description: This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms. To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest neighbours of the current indicator(s) values. The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours, sees their state and thus classifies the current point. The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. Note: TradingViews's playback feature helps to see this strategy in action. Warning: Signals ARE repainting. Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+++/Days
Indikator Pine Script™
oleh capissimo
148
Machine Learning / Longs [Experimental]Hello Traders/Programmers, For long time I thought that if it's possible to make a script that has own memory and criterias in Pine. it would learn and find patterns as images according to given criterias. after we have arrays of strings, lines, labels I tried and made this experimental script. The script works only for Long positions. Now lets look at how it works: On each candle it creates an image of last 8 candles. before the image is created it finds highest/lowest levels of 8 candles, and creates a string with the lengths 64 (8 * 8). and for each square, it checks if it contains wick, green or red body, green or red body with wicks. see the following picture: Each square gets the value: 0: nothing in it 1: only wick in it 2: only red body in it 3. only green body in it 4: red body and wick in it 5: green body and wick in it And then it checks if price went up equal or higher than user-defined profit. if yes then it adds the image to the memory/array. and I call this part as Learning Part. what I mean by image is: if there is 1 or more element in the memory, it creates image for current 8 candles and checks the memory if there is a similar images. If the image has similarity higher than user-defined similarty level then if show the label "Matched" and similarity rate and the image in the memory. if it find any with the similarity rate is equal/greater than user-defined level then it stop searching more. As an example matched image: and then price increased and you got the profit :) Options: Period: if there is possible profit higher than user-defined minimum profit in that period, it checks the images from 2. to X. bars. Min Profit: you need to set the minimum expected profit accordingly. for example in 1m chart don't enter %10 as min profit :) Similarity Rate: as told above, you can set minimum similarity rate, higher similarity rate means better results but if you set higher rates, number of images will decrease. set it wisely :) Max Memory Size: you can set number of images (that gives the profit equal/higher than you set) to be saved that in memory Change Bar Color: optionally it can change bar colors if current image is found in the memory Current version of the script doesn't check if the price reach the minimum profit target, so no statistics. This is completely experimental work and I made it for fun. No one or no script can predict the future. and you should not try to predict the future. P.S. it starts searching on last bar, it doesn't check historical bars. if you want you should check it in replay mode :) if you get calculation time out error then hide/unhide the script. ;) Enjoy!
Indikator Pine Script™
oleh LonesomeTheBlue
84
Universal Scalping BOT (USB) - With AlertsThis Study Is based on • RSI • Moving Average • Candlestick price action and • Bulls Bears calculation. This Study is Also has alerts inbuilt. Alerts are for: • Buy Active • Buy Profit Achieved • Buy Loss Hit • Sell Active • Sell Profit Achieved • Sell Loss Hit How to trade ? • When Green big Up triangle Comes, Buy that time and book profit at red small circle and book loss at yellow down small triangle. • When Red big Down triangle Comes, Sell that time and book profit at green small circle and book loss at yellow up small triangle. ► Options Available In Setting: To Show / Hide : • Target Line • Stop-Loss Line • Define Trading Sessions ► Trailing SL is calculated on candle stick price action and not on Average True Range. Test Yourself and give feedback. PM us to obtain access.
Indikator Pine Script™
oleh Algonize
15
KBL PLAY-ZONE PLOTTER - MCX CRUDE OIL ► How To Use This Indicator ? • New Intraday Trading Levels Will Be Generated At 09:30 AM (UTC +05:30) • Buy If 5 Minutes Candle Close Above '' BreakOut Buy Here '' Level. • Sell If 5 Minutes Candle Close Below '' BreakOut Sell Here '' Level. • Book Profits At Breakout Buy or BreakOut Sell Targets. • If 1st Call Target Hit , Then Do Not Trade More On That Day. • If 1st Call StopLoss Hit , Then Only Trade On 2nd Call. PM us to obtain access.
Indikator Pine Script™
oleh Algonize
13
Neural Network CrawlAbout the Indicator The Crawling Neural Network is a unique algorithm that identifies clusters of random walks that are crossing above or below the market price of the asset. The random walks always exist, but the specific series that contribute to the cluster can only be seen during their significant period. When the price trends strongly in a direction, it is more likely that it will traverse a significant amount of random walks and form a cluster. The random walks are derived from a random selection of logarithmic movements in the last 200 bars and have been spawned at the beginning of price history. Additionally, if you add the indicator to your chart multiple times and change the modifier in the settings panel, you can view more random walks that contribute to the clusters as seen in the screenshot below. This indicator is available to everyone. Enjoy...
Indikator Pine Script™
oleh GrantPeace
12
EPS AIThis indicator can be accessed by ANYONE by searching in the public indicator library located at the top of your chart! Enjoy! Introduction This indicator uses machine learning to predict the next Earnings Per Share (EPS) figure. The algorithm learns from previous figures in order to more accurately predict the next. As time continues, this indicator will become more accurate as it learns from an increased amount of data from earnings results. When the Future Projected EPS is positive, the line will appear green . When the Future Projected EPS is negative, the line will appear as red and sit below the EPS. Settings Panel The settings panel contains two tick-boxes. Quarterly Earnings : When selected, the EPS and future projected EPS will utilise quarterly results. Yearly results are used by default. Diluted EPS : When selected, the Diluted EPS and future projected Diluted EPS will be utilised. Basic EPS is used by default. Indicator Utility The EPS AI can be utilised on every securities instrument and time-frame. This indicator has been built in Pinescript V4 and will operate in real-time. This indicator can be accessed by ANYONE by searching in the public indicator library located at the top of your chart! Enjoy!
Indikator Pine Script™
oleh GrantPeace
4
NAND PerceptronExperimental NAND Perceptron based upon Python template that aims to predict NAND Gate Outputs. A Perceptron is one of the foundational building blocks of nearly all advanced Neural Network layers and models for Algo trading and Machine Learning. The goal behind this script was threefold: To prove and demonstrate that an ACTUAL working neural net can be implemented in Pine, even if incomplete. To pave the way for other traders and coders to iterate on this script and push the boundaries of Tradingview strategies and indicators. To see if a self-contained neural network component for parameter optimization within Pinescript was hypothetically possible. NOTE: This is a highly experimental proof of concept - this is NOT a ready-made template to include or integrate into existing strategies and indicators, yet (emphasis YET - neural networks have a lot of potential utility and potential when utilized and implemented properly). Hardcoded NAND Gate outputs with Bias column (X0): // NAND Gate + X0 Bias and Y-true // X0 // X1 // X2 // Y // 1 // 0 // 0 // 1 // 1 // 0 // 1 // 1 // 1 // 1 // 0 // 1 // 1 // 1 // 1 // 0 Column X0 is bias feature/input Column X1 and X2 are the NAND Gate Column Y is the y-true values for the NAND gate yhat is the prediction at that timestep F0,F1,F2,F3 are the Dot products of the Weights (W0,W1,W2) and the input features (X0,X1,X2) Learning rate and activation function threshold are enabled by default as input parameters Uncomment sections for more training iterations/epochs: Loop optimizations would be amazing to have for a selectable length for training iterations/epochs but I'm not sure if it's possible in Pine with how this script is structured. Error metrics and loss have not been implemented due to difficulty with script length and iterations vs epochs - I haven't been able to configure the input parameters to successfully predict the right values for all four y-true values for the NAND gate (only been able to get 3/4; If you're able to get all four predictions to be correct, let me know, please). // //---- REFERENCE for final output // A3 := 1, y0 true // B3 := 1, y1 true // C3 := 1, y2 true // D3 := 0, y3 true PLEASE READ: Source article/template and main code reference: towardsdatascience.com towardsdatascience.com towardsdatascience.com
Indikator Pine Script™
oleh auroagwei
14
CBOE PCR Factor Dependent Variable Odd Generator This script is the my Dependent Variable Odd Generator script : with the Put / Call Ratio ( PCR ) appended, only for CBOE and the instruments connected to it. For CBOE this script is more accurate and faster than Dependent Variable Odd Generator. And the stagnant market odds are better and more realistic. Do not use for timeframe periods less than 1 day. Because PCR data may give repaint error. My advice is to use the 1-week bars to gain insight into your analysis. This code is open source under the MIT license. If you have any improvements or corrections to suggest, please send me a pull request via the github repository github.com I hope it will help your work.Best regards!
Indikator Pine Script™
oleh Noldo
26
ANN MACD (BTC) Logic is correct. But I prefer to say experimental because the sample set is narrow. (300 columns) Let's start: 6 inputs : Volume Change , Bollinger Low Band chg. , Bollinger Mid Band chg., Bollinger Up Band chg. , RSI change , MACD histogram change. 1 output : Future bar change (Historical) Training timeframe : 15 mins (Analysis TF > 4 hours (My opinion)) Learning cycles : 337 Training error: 0.009999 Input columns: 6 Output columns: 1 Excluded columns: 0 Grid Training example rows: 301 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Network Input nodes connected: 6 Hidden layer 1 nodes: 8 Hidden layer 2 nodes: 0 Hidden layer 3 nodes: 0 Output nodes: 1 Learning rate : 0.6 Momentum : 0.8 More info : EDIT : This code is open source under the MIT License. If you have any improvements or corrections to suggest, please send me a pull request via the github repository github.com
Indikator Pine Script™
oleh Noldo
13
ANN MACD Future Forecast (SPY 1D) NOTE : Deep learning was conducted in a narrow sample set for testing purposes. So this script is Experimental . This system is based on the following article and is inspired by an external program: hackernoon.com None of the artificial neural networks in Tradingview work and are not based on completely correct logic. Unlike others in this system: IMPORTANT NOTE: If the tangent activation function is used, the input data must also have tangent values (compared to the previous values of 1 bar). Inputs were prepared according to this judgment. 1. The tangent function which is the activation function is written correctly. (The tangent function in the article: ActivationFunctionTanh (v) => (1 - exp (-2 * v)) / (1 + exp (-2 * v))) 2. Missing bias parts in the formulas were added. 3. The output function is taken from the next day (historical), so that the next bar can be predicted, which is the truth. 4.The forecast value of the next bar is subtracted from the current bar change and the market direction is determined. 5.When the future forecast and the current close are added together, the resulting data is called seed. The seed carries data both from the present and from yesterday and from the future. 6.And this seed was subjected to the MACD method. Thus, due to exponential averages, more importance will be given to recent developments and The acceleration situations will show us the direction. However, a short position should be taken for crossover and a long position for crossunder . Because the predicted values ​​work in reverse.Even though we use the same period (9,12,26) it is much faster! 7. There is no future code that can cause Repaint. However, the color after closing should be checked. The system is completely correct. However, a very narrow sample was selected. 100 data: Tangent diffs ; volume change, bollinger bands values changes (Upband , Midband , Lowband) and LazyBear's Squeeze Momentum Indicator (SQZMOM_LB) change and the next bar data (historical) price change were put into the deep learning test. IMPORTANT NOTE : The larger the sample set and the more effective dependent variables, the higher the hit rate of the deep learning test! EDIT : This code is open source under the MIT License. If you have any improvements or corrections to suggest, please send me a pull request via the github repository github.com Stay tuned. Best regards!
Indikator Pine Script™
oleh Noldo
15
Dependent Variable Odd Generator For Machine Learning TechniquesCAUTION : Not suitable for strategy, open to development. If can we separate the stagnant market from other markets, can we be so much more accurate? This project was written to research it. It is just the tiny part of the begining. And this is a very necessary but very small side function in the main function. Lets start : Hi users, I had this idea in my mind for a long time but I had a hard time finding the parameters that would make the market stagnant. This idea is my first original command system. Although it is very difficult to make sense of the stagnant market, I think that this command system can achieve realistic proportions. With 's money flow index, I opened the track to determine the level. On the other hand, the prices were also using a money flow index, and it forced me to make the limitations between the levels in a logical way. But the good thing is that since the bollinger bandwidth uses a larger period, we are able to print normal values ​​at extreme buy and sell values. In terms of price, we can define excessive purchase and sale values ​​as the period is smaller. I have repeatedly looked at the limit values ​​that determine the bull, bear, and bollinger bandwidth (mfi), and I think this is the right one. Then I have included these values ​​in the probability set. The bull and bear market did not form the intersection of the cluster, and because there are connected events, the stagnant market, which is the intersection, will be added to the other markets with the same venn diagram logic and the sum of the probability set will be 1. is equal to. I hope that we can renew the number generators in the very important parameters of machine learning such as Markov Process with generators dependent on dependent variables, which bring us closer to reality. This function is open to development and can be made of various ideas on machine learning. Best wishes. This code is open source under the MIT license. If you have any improvements or corrections to suggest, please send me a pull request via the github repository github.com
Indikator Pine Script™
oleh Noldo
3
Megalodon Why Megalodon? We believe that enlightening others is an incredible way to make this world a better place. That's why we created the tools you need to stop worrying about your investments and focus on what really matters in your life. What is Megalodon? Megalodon uses Artificial Intelligence that combines 574 back-tested indicators and 2674 back-tested setups, simultaneously. Megalodon works with any kind of asset , market state and time frame . What is the win rate? Megalodon is extremely accurate and offers insane profits as long as the fundamental analysis is done right . Backtesting results can be found on our social media or down below . How to use Megalodon Pro Automated Trader? Simply, orange rectangles are buy signals and purple rectangles are sell signals . Green clouds show buy signals and red clouds show sell signals. Yellow line shows the difference between buy and sell counts. How to set alarms on Megalodon Pro Automated Trader? Click on Alert, select Megalodon Pro Automated Trader and click on Buy-Signal or Sell-Signal. More details can be found on our social media. You may also watch our Megalodon Investing Tutorials on Youtube for more information. How to purchase? Megalodon is totally FREE . You may upgrade to Megalodon Pro for the most important features , including automizing your trading on any asset profitably, setting up alarms to get notified , joining to our VIP telegram channel to get daily updates and our VIP telegram group to interact with the community. Simply go to our website on our TradingView page for learning more and joining us for free. Some of the backtesting results are: BTC/USD for longer time frame trading in the bear market for the last year. APPLE for longer time frame trading in the bull market for the last 10 years. EURO/USD for day trading in the neutral market for the last month. BTC/USD for day trading in the bull market for the last 15 days. APPLE for day trading in the neutral market for the last 2 days and 6 hours.
Indikator Pine Script™
oleh MegalodonTrading
102
Megalodon Pro Automated Trader Why Megalodon? We believe that enlightening others is an incredible way to make this world a better place. That's why we created the tools you need to stop worrying about your investments and focus on what really matters in your life. What is Megalodon? Megalodon uses Artificial Intelligence that combines 574 back-tested indicators and 2674 back-tested setups, simultaneously. Megalodon works with any kind of asset , market state and time frame . What is the win rate? Megalodon is extremely accurate and offers insane profits as long as the fundamental analysis is done right . Backtesting results can be found on our social media or down below . How to use Megalodon Pro Automated Trader? Simply, orange rectangles are buy signals and purple rectangles are sell signals . Green clouds show buy signals and red clouds show sell signals. Yellow line shows the difference between buy and sell counts. How to set alarms on Megalodon Pro Automated Trader? Click on Alert, select Megalodon Pro Automated Trader and click on Buy-Signal or Sell-Signal. More details can be found on our social media. You may also watch our Megalodon Investing Tutorials on Youtube for more information. How to purchase? Megalodon is totally FREE . You may upgrade to Megalodon Pro for the most important features , including automizing your trading on any asset profitably, setting up alarms to get notified , joining to our VIP telegram channel to get daily updates and our VIP telegram group to interact with the community. Simply go to our website on our TradingView page for learning more and joining us for free. Some of the backtesting results are: BTC/USD for longer time frame trading in the bear market for the last year. APPLE for longer time frame trading in the bull market for the last 10 years. EURO/USD for day trading in the neutral market for the last month. BTC/USD for day trading in the bull market for the last 15 days. APPLE for day trading in the neutral market for the last 2 days and 6 hours.
Indikator Pine Script™
oleh MegalodonTrading
38
Intelligent Supertrend (AI) - Buy or Sell SignalIntroduction This indicator uses machine learning (Artificial Intelligence) to solve a real human problem. The artificial intelligence that operates this Supertrend was created by an algorithm that tests every single combination of input values across the entire chart history of an instrument for maximum profitability in real-time. The Supertrend is one of the most popular indicators on the planet, yet no one really knows what input values work best in combination with each other. A reason for this is because not one set of input values is always going to be the best on every instrument, time-frame, and at any given point in time. The "Intelligent Supertrend" solves this problem by constantly adapting the input values to match the most profitable combination so that no matter what happens, this Supertrend will be the most profitable. Indicator Utility The Intelligent Supertrend does not change what has already been plotted and does not repaint in any way which means that it is fully functional for trading in real-time. Ultimately, there are no limiting factors within the range of combinations that have been programmed. The Supertrend will operate normally but will change input values according to what is currently the most profitable strategy. Input Values While a normal Supertrend would include two user-defined input values, the Intelligent Supertrend automates the input values according to what is currently the most profitable combination. Additional Tools The Optimised Supertrend is a tool that can be used to visual what input values the Supertrend AI is currently using. Additional tools to back-test this indicator will be added to this product soon. For more information on how this indicator works, view the documentation here: www.kenzing.com For more information on the Supertrend view these fun facts: www.marketcalls.in
Indikator Pine Script™
oleh GrantPeace
43
Intelligent Volume-weighted Moving Average (AI)Introduction This indicator uses machine learning (Artificial Intelligence) to solve a real human problem. The volume-weighted moving average (VWMA) is one of the most used indicators on the planet, yet no one really knows what pair of volume-weighted moving average lengths works best in combination with each other. A reason for this is because no two VWMA lengths are always going to be the best on every instrument, time-frame, and at any given point in time. The "Intelligent Volume-weighted Moving Average" solves the moving average problem by adapting the period length to match the most profitable combination of volume-weighted moving averages in real time. How does the Intelligent Volume-weighted Moving Average work? The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time. No matter what happens, the combination of these volume-weighted moving averages will be the most profitable. Can we learn from the Intelligent Volume-weighted Moving Average? There are many lessons to be learned from the Intelligent VWMA. Most will come with time as it is still a new concept. Adopting the usefulness of this AI will change how we perceive moving averages to work. Limitations This indicator does not change what has already been plotted and does not repaint in any way shape or form which means it is excellent for trading in real-time! Ultimately, there are no limiting factors within the range of combinations that has been programmed. The volume-weighted moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't? Thresholds The range of VWMA lengths is between 5 to 40. The black crosses can be turned off in the settings panel. Test this indicator! I am also publishing tools that can be used to back-test this indicator and understand what period length is currently being used. There will be many more updates to come so stay tuned! Updated documentation and access to this indicator can be found at www.kenzing.com
Indikator Pine Script™
oleh GrantPeace
19
Intelligent Exponential Moving Average (AI)Introduction This indicator uses machine learning (Artificial Intelligence) to solve a real human problem. The Exponential Moving Average (EMA) is one of the most used indicators on the planet, yet no one really knows what pair of exponential moving average lengths works best in combination with each other. A reason for this is because no two EMA lengths are always going to be the best on every instrument, time-frame, and at any given point in time. The "Intelligent Exponential Moving Average" solves the moving average problem by adapting the period length to match the most profitable combination of exponential moving averages in real time. How does the Intelligent Exponential Moving Average work? The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time. No matter what happens, the combination of these exponential moving averages will be the most profitable. Can we learn from the Intelligent Moving Average? There are many lessons to be learned from the Intelligent EMA. Most will come with time as it is still a new concept. Adopting the usefulness of this AI will change how we perceive moving averages to work. Limitations Ultimately, there are no limiting factors within the range of combinations that has been programmed. The exponential moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't? Thresholds The range of exponential moving average lengths is between 5 to 40. Additional coverage resulted in TradingView server errors. Future Updates! Soon, I will be publishing tools to test the AI and visualise what moving average combination the AI is currently using.
Indikator Pine Script™
oleh GrantPeace
10
Optimised Exponential Moving AverageAbout This tool measures the profitability of every exponential moving average length combination for the entire history of the instrument that it is applied and only displays the most profitable combination in real-time meaning that this indicator is fully functional for trading. The Optimised Exponential Moving Average can be tested using an Exponential Moving Average strategy and the Strategy Tester panel on any instrument or time-stamp. It will always display the lengths of the most profitable exponential moving average lengths at the current moment in time. This can be used on its own, or paired with the Intelligent Exponential Moving Average (AI) for a better understanding of the indicators movements. The Intelligent Exponential Moving Average (AI) uses this tool as a predictive method for machine learning.
Indikator Pine Script™
oleh GrantPeace
10