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Know the Entry and Exit of stocks using DOW theory

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  Follow the below steps to find the Entry and Exit Do the fundamental analysis find some best stocks Pick the stock and draw the above DOW lines and see if the market it uptrend, downtrend and sideways. If the market is Uptrend with Big Green Candles  then it suggests to take entry. Entry And Exit :  Chart in day view and check if the previous two days have  consecutive green candles  and second candle should start with "high of the first candle".  If yes then take entry at the high of the second handle(Previous day) and stop loss at the low of the second handle. And then change the stop loss to the upper level when your  profits are increasing.
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TF01 : Understanding Neural Networks using TensorFlow

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Objective: The goal is to develop a machine-learning model capable of converting Celsius to Fahrenheit. This model will be trained using a dataset containing pairs of Celsius and Fahrenheit values. The conversion will be based on the formula   F=C×1.8+32 . After training, the model’s performance will be evaluated by comparing its predictions with the results obtained from the formula. Below are the important steps for building a Neural network model Import dependencies   - Tensorflow , numpy , etc Setup Training data -  Features - Input Labels - output Examples - Input/output pairs for model training. Create a neural network model Build a layer Assemble layers into the model Compile the model with loss and optimization functions Train the model -  The training model is about tuning the internal variables of the networks to the best possible values so that they can map the input to the output Use the model to predict the weights Example Code which you can paste...
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TF03 : Go further on Convolutional Neural Networks

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Convolution neural networks can also help to classify colored images There are two challenges when dealing with colored images Colored images are of different sizes. Images of different colors Colored images have different sizes and colors, unlike the Fashion MNIST dataset with images of the same size 28X28 with one color(Greyscale). How are we handling different sizes of images ..? The neural network needs a fixed-size input, so we will resize all colored images to fixed-size. Here we are converting images to 150X150 and flattened for neural network input. How are we handling the different colors of the image ..? If a greyscale image is 6X6 then it is converted into a matrix of size 6X6 but in the colored image, we will have three dimensions. Color images are represented by three color channels - Red,Blue, Green. All three colored channels are combined to form a colored image and each of these colored channels is represented by a 2-dimensional array. So the depth of the image is a sta...
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