Deep_learning — インジケーターとストラテジー

Historical Similarities Happy trading! This tool provides short-term trend estimations. It is a further evolution of my earlier ANN Trend Prediction indicator, but it uses a completely new feature-vector composition and a different type of neural network. 1. Concept The underlying idea is that history tends to repeat—not exactly, but with recognizable similarities. When recent market conditions resemble past situations, it is reasonable to assume that price may behave similarly again. That is the foundation of this indicator. In the image below, you can see the general setup. The most recent bar (the “now” point) separates the past from the future. A sequence of recent bars is interpreted as a pattern and fed into a pre-trained LSTM network, which then produces the prediction for the current bar. The focus of this tool is to deliver predictions as early as possible—ideally just before a trend reversal—to support short-term trades lasting approximately 5 to 20 bars. While perfect early detection is not reached here, this indicator often identifies reversals within one bar after they occur, which is usually early enough to capture meaningful moves. There are other indicators capable of signaling trend reversals within a single bar—such as Shooting Star or Hammer candle patterns, or certain indicator setups. They were effective when they were new, but widespread use has reduced their reliability, and sometimes those patterns simply do not appear or appear without trend reversal. This tool, by contrast, is new and it successfully identifies many trend reversals, as demonstrated in the image below: 2. Experimental Part Furthermore, because this approach offers multiple settings that influence its behavior, you can configure it to focus on larger trends and ignore smaller fluctuations. The following images show several examples: The default settings with only-Body Smoothing enabled with Generalized Trends enabled with both Smoothing enabled However, as you may notice, when targeting larger trends, a number of false-negative predictions may also appear. These still need to be filtered out. Please keep in mind that this version is experimental, requiring further investigation and research, and I would appreciate any feedback or suggestions. 3. Results The prediction output is shown through a label and background colors as shown in the following image. It provides probabilities for three market directions: Up - green Sideways - blue Down - red When the model cannot confidently classify the current market conditions, it deliberately withholds a prediction and leaves the background uncolored. In most cases, however, it displays a label with all three probabilities whenever a new dominant prediction emerges, and it remains visible until the next dominant signal appears. 4. Conclusion In its default settings, this indicator is quite capable when short-term trends last at least five or more bars. A support-and-resistance indicator can be helpful for setting take-profit and stop-loss levels. 5. Settings Menu The script is delivered with its default settings, all turned off. However, several configuration options are available: Input Preparation Smoothing – Using Heikin Ashi or using only Bar Body are two methods that help remove outliers from the incoming bars. Generalize Trends – Merges nearby trends together and removes smaller, insignificant trends. Generalize Patterns – Checks the incoming pattern for artifacts and reduces it when possible. This is a trade-off between removing noise and keeping meaningful features. Shifting – Examines the incoming pattern for consistency and adjusts it when reasonable. Speed – Determines how quickly a prediction is calculated. A longer calculation time can improve accuracy, but may also risk the script timing out. Filters Intrabar – Off by default, allowing new dominant predictions only at bar close. When enabled, predictions may also be updated intrabar, but this introduces repainting on the current open bar. Start of Classification – Sets the date from which classifications may begin. Minimum Gains After Pattern – Defines how much price must rise or fall for a pattern to be considered an up, down, or sideways pattern. Minimum Classification Probability – If the highest probability is below this threshold, no prediction is selected and the background stays translucent. If one probability exceeds the threshold, the largest one is chosen as the prediction. Minimum Pattern Strength – Removes patterns whose strength is below this threshold. 6. Alert Signals Available Trend Signal 2 = possible High 1 = Uptrend 0 = Ranging -1 = Downtrend -2 = possible Low na = no prediction Signal Age - counts the number of bars since last change 7. Declaration for TradingView House Rules on Script Publishing The unique feature of the Historical Similarities Indicator is it's nearby real-time detection capability for up trends, down trends and side way price action in most cases. This script is closed-source and invite-only, to support and compensate for years of development work. 8. Disclaimer Trading involves risk, and losses can and do occur. This script is intended for informational and educational purposes only. All examples are hypothetical and not financial advice. Decisions to buy, sell, hold, or trade securities, commodities, or other assets should be based on the advice of qualified financial professionals. Past performance does not guarantee future results. Use this script at your own risk. It may contain bugs, and I cannot be held responsible for any financial losses resulting from its use. Cheers!

ANN TREND SPX500 1m-1H Happy Trading! This indicator is the successor to my previous ANN Trend Prediction, now featuring improved feature vectors, refined backpropagation, and a stronger focus on asset- and timeframe-specific patterns for more precise predictions. Internally is a collection of nine artificial neural networks (ANNs) trained on the S&P 500 to forecast uptrends, downtrends, or ranging markets. Each ANN is trained on one of the following timeframes: 1m, 2m, 3m, 5m, 10m, 15m, 30m, 45m, and 60m, and the appropriate model is selected automatically. 1. Settings In the settings menu shown in the image below, you’ll find six options: Indicator Timeframe – Choose between 1m and 1H. Intrabar – Choose between Alerts been send intrabar or only at bar closing. Lookback – Define how many previous bars the ANN should use in its calculations. Smoothing – To reduce short-term switching of the prediction you can activate Smoothing. Here-by the input datas get filtered by a mean function. Range Filter – Enable a third class, Ranging, in addition to Uptrend and Downtrend. This enables you to avoid choppy markets. Class Colors – Here you can change each Class (Up, Down Trend etc) color. 2. Comparison with EMA crossover The Prediction of the ANN Trend SPX500 1m-1H is more reliable as the prediction of the EMA crossover, shown in the Image below. Both indicators use the same period of 65 bars and source their input data from the same chart. While the EMA crosses over multiple times (shown as red vertical lines in the image), the ANN Trend maintains its prediction signal as Uptrend. This advantage of the ANN comes from its learned knowledge. During training, it was exposed to a vast number of price charts, enabling it to distinguish between a trend setback and a true trend reversal. 3. Alerts The indicator generates two types of alert signals: Trade Signal: 1 = Uptrend 0 = Ranging -1 = Downtrend -2 = no prediction Signal Age: Counts the number of bars since the last signal change. With the Signal Age you have access to the entry-price of the actual Trend. If you use You just call close to get the last entry-price. 4. Declaration for TradingView House Rules on Script Publishing The unique feature of ANN Trend SPX500 1m-1H is it's real-time range detection capability and it's capability to distinguishes between a Trend set back and a Trend reversal which results in longer lasting trend predictions in comparison to any Moving Average Crossover Indicators. This script is closed-source and invite-only, to support and compensate for months of development work. 5. Disclaimer Trading involves risk, and losses can and do occur. This script is intended for informational and educational purposes only. All examples are hypothetical and not financial advice. Decisions to buy, sell, hold, or trade securities, commodities, or other assets should be based on the advice of qualified financial professionals. Past performance does not guarantee future results. Use this script at your own risk. It may contain bugs, and I cannot be held responsible for any financial losses resulting from its use. Cheers!

ANN MACD ETHEREUM This script is trained with Ethereum (Timeframe : 4 hours ). Details : Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 300 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Input nodes connected: 19 Hidden layer 1 nodes: 8 Hidden layer 2 nodes: 1 Hidden layer 3 nodes: 0 Output nodes: 1 Learning rate: 0.7000 Momentum: 0.8000 Training error: 0.009378 ( That's a very good error coefficient. ) Many thanks to wroclai for help. Deep learning series will continue!

ANN MACD BTC v2.0 This script is the 2nd version of the BTC Deep Learning (ANN) system. Created with the following indicators and tools: RSI MACD MOM Bollinger Bands Guppy Exponential Moving Averages: (3,5,8,10,12,15,30,35,40,45,50,60) Note: I was inspired by the CM Guppy Ema script. Thank you very much to dear wroclai for his great help. He has been a big help in the deep learning series. That's why the licenses in this series are for both of us. I'm sharing these series and thats the first. Stay tuned and regards! Note : Alerts added.

SPY FRACTAL S-R LEVELS (FIXED ANN MACD) This is a fractal version of my deep learning script for SPY In addition, buy and sell conditions may appear in bar colors in green and red. You can choose from the menu if you wish. Fractal codes do not belong to me. So I didn't put any license. You can use it as you want, you can change and modify. Regards.Noldo

BTC FRACTAL ANN S-R LEVELS (Fixed ANN MACD) This script is an adaptation of my deep learning system for Bitcoin to fractals. Fractal codes are not belong to me. Original : The code for the Deep learning (ANN MACD BTC) work belongs to me. Original: I didn't get license for this script because the fractal codes don't belong to me.You can use it for any purpose. This command can be a very helpful guide.You can use that fractals with your indicators for Bitcoin. You can also combine these levels with ANN - MACD - BTC script. Scripts about Artificial Neural Networks (ANN) will continue soon ! I hope it will help us to gain insight into technical analysis. Best regards. Noldo.