Can I contribute on this project by adding some reading list and useful website??? And later also suggest some ways to predict stock with genetic algorithm?

any plans to use the jupyter notebook? https://jupyter.org/ graphs e.g. pyplot etc are somewhat easier with that, but that it would probably consume significantly more memory and resources

i did a new test of PredictMarketWithBayesTheorem in my repository PredictMarketWithBayesTheorem.ipynb

the main things i changed are normally log returns is computed as log(today's price) - log(yesterday's price) = log ( today's price / yesterday's price ). hence this formula is used instead and for the categories i used a histogram to define them, scroll below for the histogram

this time round there is a difference, what is most interesting is the HL and HG predictions

I really liked the way you are working on this project, by not just using the Deep Learning models but also adding traditional technical analysis to it. Can I know why you are working on this? An open-Source Consultation, or a Research Project, an assignment, or just out of interest?

Hello @timestocome I have started researching upon stock market prediction and came across your project. Thank You for your work, but I don't understand why you just update the README all the time :P ?

LSTM may not be suitable for predicting trend in a short stock market period.

I am attempting to classify the shape of a stock(last N days OHLCV data, 5 days for instance) into M kinds of classification (7 output classes).

Such as drop -8% of stock price as a class, drop [-8%, -4%) as another class, etc.

As you can see, the training accuracy is high, and definitely overfitting: Accuracy for training classes: (7 classes) [ 82.609 84.146 82.596 87.204 84.164 77.381 80. ]

Where as the predict accuracy is low: Accuracy for predict following 20 days [ nan 0. 20. 57.143 50. 0. nan]

For now, I am wondering if change the classification model to DBN could produce a more reasonable data outcome. Hope it helps.

Below is part of the out fragment:

Epoch 1996/2000 81581/81581 [==============================] - 58s - loss: 0.0157 - acc: 0.9954 Epoch 1997/2000 81581/81581 [==============================] - 58s - loss: 0.0139 - acc: 0.9957 Epoch 1998/2000 1650/81581 [..............................] - ETA: 57s - loss: 0.0173 - acc: 0.9958 81581/81581 [==============================] - 58s - loss: 0.0143 - acc: 0.9954 Epoch 1999/2000 81581/81581 [==============================] - 58s - loss: 0.0153 - acc: 0.9954 Epoch 2000/2000 5050/81581 [>.............................] - ETA: 55s - loss: 0.0176 - acc: 0.9943 81581/81581 [==============================] - 58s - loss: 0.0158 - acc: 0.9951 save LSTM model... ############## validation on test data ############## scaled data mse: 0.130540770636 load LSTM model... ############## validation on train data ############## scaled data mse: 0.0391746699673 ############## validation on valid data ############## scaled data mse: 0.176731004083 ############## validation on lately data ############## scaled data mse: nan

---------- AMD ----------

classification counter: [23, 82, 339, 422, 341, 84, 30] classification possibility: [ 1.741 6.207 25.662 31.945 25.814 6.359 2.271] classification train predict: [ 82.609 84.146 82.596 87.204 84.164 77.381 80. ] classification valid predict: [ nan 0. 20. 57.143 50. 0. nan]

                   close     volume      predict_profit  a_+1_d  p_+1_d      

Date 2017-03-15 13.98 54885200 -2.360515 -1.0 -2.0 2017-03-16 13.65 44129100 -1.172161 -1.0 -2.0 2017-03-17 13.49 218636000 6.745738 2.0 1.0
2017-03-20 14.40 90863900 -4.027778 -2.0 0.0 2017-03-21 13.82 72191500 2.026049 1.0 1.0 2017-03-22 14.10 61089400 -2.198582 -1.0 -1.0 2017-03-23 13.79 44144100 -0.652647 0.0 0.0 2017-03-24 13.70 49903700 0.000000 0.0 0.0 2017-03-27 13.70 42537800 -0.072993 0.0 2.0 2017-03-28 13.69 37005800 0.146092 0.0 0.0 2017-03-29 13.71 37777200 2.479942 1.0 -1.0
2017-03-30 14.05 43814100 3.558719 1.0 -1.0
2017-03-31 14.55 84362600 0.618557 0.0 0.0
2017-04-03 14.64 48299200 -3.278689 -1.0 1.0
2017-04-04 14.16 58217200 0.070621 0.0 -2.0 2017-04-05 14.17 58384000 -6.351447 -2.0 2.0 2017-04-06 13.27 139038000 1.883949 1.0 1.0
2017-04-07 13.52 70297900 -3.106509 -1.0 1.0 2017-04-10 13.10 46924500 0.000000 0.0 1.0
2017-04-11 13.10 59786900 -2.595420 -1.0 0.0 2017-04-12 12.76 37087100 NaN NaN 0.0