St Louis
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14 min readKeltner Channels are a technical analysis tool used by traders to analyze and interpret potential price reversals, overbought or oversold conditions, and market volatility. Developed by Chester W. Keltner, the concept combines elements of moving averages and average true range to create a charting indicator.To interpret Keltner Channels, you need to understand the three components that make up the indicator: the middle line, the upper band, and the lower band.
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9 min readCustom loss functions can be implemented in TensorFlow to train machine learning models with specific objectives or requirements. TensorFlow provides flexibility in defining and using custom loss functions by allowing users to create their own loss functions using its computational graph framework.
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15 min readThe Elder-Ray Index is a technical analysis tool developed by Alexander Elder to help traders and investors identify possible trading opportunities in the financial markets. It consists of two components: the Bull Power and the Bear Power.The Bull Power is calculated by subtracting the Exponential Moving Average (EMA) of the daily lows from the daily closes. It measures the strength of the bulls or buyers in the market.
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6 min readTo use GPU with TensorFlow for faster training, you need to follow the following steps:Install necessary components: Install CUDA Toolkit: TensorFlow requires CUDA to utilize the GPU. Install the appropriate version of CUDA Toolkit from the NVIDIA Developer website. Install cuDNN: TensorFlow also needs cuDNN (CUDA Deep Neural Network library) for accelerated GPU training.
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10 min readTrading with acceleration bands for scalping can be an effective strategy for capturing short-term price movements in the market. Acceleration bands are a technical indicator that helps traders identify potential turning points in the price action. They consist of three bands: an upper band, a lower band, and a middle band.
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6 min readSaving and loading a trained model in TensorFlow involves the use of the tf.train.Saver() class. The steps to save and load a trained model are as follows:Import the necessary libraries: import tensorflow as tf Define the model architecture and train it. Once the model is trained, you can obtain the weights and biases. Create a tf.train.Saver() object to save and load your model: saver = tf.train.
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13 min readMoving Max is a technical indicator that is frequently used in day trading to identify trends and potential trading opportunities. It is calculated by taking the highest value in a given period and using it as a reference point. Although there are various ways to use Moving Max, here is a general understanding of how it can be utilized in day trading:Identifying trend reversals: Moving Max can help traders spot potential trend reversals in the market.
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10 min readTraining a model in TensorFlow involves several steps, including:Defining the model architecture: Start by defining the structure of your model, including the layers, their connections, and the activation functions. You can choose from various types of layers, such as dense (fully connected), convolutional, recurrent, etc. Creating a loss function: Specify a loss function that measures how well your model performs.
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9 min readLoading and preprocessing data is an essential task in machine learning workflows, as it involves preparing the data for training and evaluation. TensorFlow provides various tools and utilities to simplify the process of loading and preprocessing data. Here is an overview of how to accomplish this in TensorFlow.Import the necessary libraries: Begin by importing the required TensorFlow libraries, such as tensorflow and tensorflow.keras.
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12 min readCreating user interfaces in Kotlin involves defining and structuring the interaction and visual components of an application. It typically involves using XML or programmatically creating UI elements and binding them with data and functionality.To create user interfaces in Kotlin, you can start by defining the layout of your screen using XML or using the Kotlin code directly. XML layout files can be created in the "res/layout" directory of your project.
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6 min readTo create a simple neural network using TensorFlow, follow these steps:Import the necessary libraries: You need to import TensorFlow and any other required libraries like numpy for numerical computations. Define the dataset: Prepare the dataset that you want to train your neural network on. Split the dataset into training and testing sets. Preprocess the data: Normalize or scale the input data to ensure that it falls within a small range.