Posts (page 177)
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13 min readOn-Balance Volume (OBV) is a technical analysis indicator that helps traders and investors to gauge the flow of volume in a particular asset, such as a stock or cryptocurrency. It was developed by Joseph Granville in the 1960s.OBV is calculated by adding the volume on up days and subtracting the volume on down days. It is based on the premise that volume precedes price movement. The OBV indicator is represented by a line that fluctuates above and below a centerline.
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10 min readAttention mechanisms in TensorFlow can be implemented to enhance the performance of deep learning models, particularly in tasks involving sequence data such as natural language processing and time series analysis. The key idea behind attention mechanisms is to selectively focus on different parts of the input sequence when making predictions, enabling the model to effectively capture important patterns and dependencies.
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8 min readAutomating a trading strategy involves using computer programs or algorithms to execute trades on your behalf based on pre-defined rules and conditions. Here are a few steps to help you understand how to automate a trading strategy:Define your trading strategy: Start by developing a clear and well-defined trading strategy that specifies the rules for entering and exiting trades.
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12 min readAverage True Range (ATR) is a technical indicator used in financial markets to measure market volatility. It helps traders and investors gauge the level of price fluctuation in a particular asset, which is valuable information for various trading strategies and risk management.To calculate the Average True Range, the following steps are involved:Calculate the True Range (TR) for each period: The true range is the maximum value of three calculations: a. Current high minus current low b.
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6 min readEmbeddings in TensorFlow are matrix representations used to encode high-dimensional data into a lower-dimensional space. They are commonly used in natural language processing (NLP) tasks, such as word or sentence embeddings.To use embeddings in TensorFlow, you need to follow these general steps:Preprocess your data: Convert your input data (e.g., textual data) into a numerical format suitable for embedding.
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10 min readOptimizing a trading strategy for risk management is essential for successful trading and minimizing potential losses. Here are some key points to consider:Define risk tolerance: It is crucial to determine your risk tolerance level based on your financial situation, experience, and investment goals. This will help you set realistic expectations and avoid taking excessive risks. Set stop-loss orders: Implementing stop-loss orders is an effective risk management technique.
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17 min readThe Force Index (FI) is a technical analysis tool used in swing trading that combines both price movement and volume to measure the strength of a trend. It was developed by Alexander Elder, a well-known trader and author.The Force Index represents the bulls' and bears' power in the market. It helps traders identify and confirm trend reversals and continuation patterns. The FI is displayed as a histogram that oscillates above and below a centerline.
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10 min readSequence models in TensorFlow can be implemented using various different techniques and approaches. One common approach is to use Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) cells. These cells are designed to handle sequential data by capturing dependencies and patterns over time.
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11 min readBuilding a trading strategy using fundamental analysis involves analyzing various factors that can impact the value and performance of a financial instrument, such as a stock or currency. This approach aims to identify the intrinsic value of the asset by examining its underlying fundamentals, such as macroeconomic indicators, financial statements, industry trends, and market sentiment.
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6 min readHyperparameter tuning is an essential aspect of optimizing the performance of a machine learning model. TensorFlow, being a popular deep learning framework, offers various techniques for tuning hyperparameters to improve the model's accuracy and generalization. Here's a brief overview of how to perform hyperparameter tuning in TensorFlow:Define the hyperparameters: Hyperparameters are the variables that determine the behavior of the training process.
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14 min readCandlestick patterns are a way of interpreting price action in financial markets, particularly in technical analysis. They are formed by the visual representation of price movements on a chart, using candle-shaped structures.Each candlestick represents a specific time interval, such as one minute, one hour, or one day, and contains four essential components: the opening price, the closing price, the highest price, and the lowest price.
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5 min readTo convert a TensorFlow model to TensorFlow Lite, you can follow these steps:Import the necessary libraries: Start by importing the required TensorFlow and TensorFlow Lite libraries. Load the TensorFlow model: Load your pre-trained TensorFlow model that you want to convert. Create a TensorFlow Lite converter: Instantiate a tf.lite.TFLiteConverter object to convert the TensorFlow model.