Posts (page 178)
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9 min readCreating a trading strategy based on technical analysis involves using historical price and volume data of an asset to make predictions about its future performance. Here is a step-by-step guide on how to create such a strategy:Understand the Basics: Familiarize yourself with the key concepts of technical analysis, such as support and resistance levels, trendlines, chart patterns, moving averages, oscillators, and indicators.
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9 min readThe Average Directional Index (ADX) is a popular technical indicator used by traders to assess the strength of a trend and potential trading opportunities. While its main purpose is to measure the strength of a trend, it can also be useful in determining whether a market is trending or ranging.To begin trading with the Average Directional Index as a beginner, there are a few key points to keep in mind. First, it's important to understand the components of the ADX.
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7 min readData augmentation is a technique commonly used in deep learning to artificially expand the training dataset by generating new examples. This approach helps to improve the model's ability to generalize and enhances its performance. TensorFlow, a popular deep learning framework, provides several methods to implement data augmentation effectively.
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7 min readBacktesting a trading strategy involves evaluating the performance and effectiveness of a trading strategy using historical data. It helps traders and investors understand how a strategy would have performed under past market conditions before implementing it in real-time trading.To backtest a trading strategy, follow these general steps:Define the Strategy: Clearly define the trading strategy, including entry and exit rules, position sizing, risk management, and any other relevant parameters.
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9 min readA Complete Guide to Fibonacci Retracements for swing trading involves using a mathematical sequence known as the Fibonacci sequence in conjunction with technical analysis tools to identify potential levels of support and resistance in the financial markets.The Fibonacci sequence, discovered by Leonardo Fibonacci in the 13th century, is a series of numbers in which each number is the sum of the two preceding numbers (e.g., 1, 1, 2, 3, 5, 8, 13, 21, etc.).
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10 min readFine-tuning a pre-trained model in TensorFlow involves taking a model that has already been trained on a large dataset and adapting it to a new, specific task or dataset. It is common to use pre-trained models as they offer a head-start in solving complex problems and can save significant computational resources.The first step in fine-tuning is to choose a suitable pre-trained model that aligns with your specific task or dataset.
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12 min readDeveloping a profitable trading strategy requires careful analysis, planning, and continuous adaptation to market conditions. Here are some key components to consider:Research and Analysis: Begin by thoroughly researching the financial markets and chosen trading instruments. Consider both technical and fundamental analysis to understand market trends, price patterns, and key economic indicators affecting your chosen assets. Objective Setting: Clearly define your trading goals and objectives.
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18 min readKeltner Channels is a technical analysis indicator that is commonly used in swing trading. It was developed by Chester Keltner and is based on the concept of volatility and average true range (ATR). Here are the basics of Keltner Channels for swing trading:Calculation: Keltner Channels consist of three lines plotted on a price chart. The middle line is usually a simple moving average (SMA) of the price over a specific period, often 20 periods.
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7 min readCallbacks are a feature in TensorFlow that allow you to perform certain actions during the training process. One common use of callbacks is for implementing early stopping, which helps prevent overfitting and saves computational resources by stopping training when the model's performance stops improving.To use callbacks for early stopping in TensorFlow, you need to define a callback object and pass it to the .fit() function during model training.
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8 min readOverfitting is a common problem that occurs when a machine learning model learns the training data too well, to the point that it fails to generalize well to new, unseen data. TensorFlow offers several techniques to handle overfitting in models.One approach is to use regularization techniques such as L1 and L2 regularization, which add a penalty term to the loss function during training.
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10 min readTrading with Typical Price involves using a simple technical indicator to analyze price movements and make trading decisions. The Typical Price is calculated by averaging the high, low, and closing price of a trading instrument over a specified period.To trade with the Typical Price, here is a general guideline:Calculate the Typical Price: Add the high, low, and closing price of the trading instrument and divide the sum by 3. This will give you the Typical Price for a specific period.
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7 min readBatch normalization is a technique used to improve the speed, stability, and performance of neural networks. It works by normalizing the output of the previous layer within each batch of training examples. This helps in mitigating the issue of internal covariate shift, where the distribution of the input to each layer changes during training.To implement batch normalization in TensorFlow, follow these steps:Import the necessary TensorFlow libraries: import tensorflow as tf from tensorflow.keras.