philipxjm / deep-convolution-stock-technical-analysis Goto Github PK

can you add back "fromCharacterToBinary" funciton back?

thanks

I have already trained and saved checkpoints, but can you show me how to use the checkpoint to use the trained model? Sorry I am new to python and CNN

Hi
@ line 103, may I know if it's typo? since the keep_prob will be 0 when dropout is 1, it's strange.

      if dropout > 0.0:
        output = tf.nn.dropout(output, keep_prob=1 - dropout)

In the README "python stock-model.py" should be "python stock_model.py"

What if, instead of simply using the price history, we included indicators such as RSI, MACD, Polarized Fractal Efficiency, etc? Should that not improve the accuracy? I'd like to investigate ways of incorporating such extra features. Any thoughts on accomplishing this?

I am very glad to find somebody focusing on Stock Market with DL technique. Before finding this github, I also pay some time on this and beat with some problems, this code gives me a good reference.
Unfortunately, I find a fatal bug that indicates this github's CNN codes cannot run in reality. That is

# shuffling the data
perm = np.arange(labels_set.shape[0])
np.random.shuffle(perm)
stocks_set = stocks_set[perm]
labels_set = labels_set[perm]

The reason for that, in reality, stock stream comes with time order, this shuffle operation will break this rule.
If you do not believe, you can comment these codes and run it, you would find your test accuracy dramatically decrease from 70% down to 50%. That means it can only behave well on train set but always sucks on test set.

TL;DR, stock price/index prediction by CNN is nearly a hard (even impossible) task, because it is essentially event-driven not data-driven (price-driven). The codes here belongs to data-driven (price-driven) methods.
By the way, some researchers now pay much more attention on event-driven method, i.e., they grabbed event data of daily news and then use Deep Learning model to analysis it. But this method is relatively complicated.

I'm self-studying to do some technical analysis. One outdate tf command appears and I can't fix it. The command is tensorflow.contrib.learn.python.learn.base.datasets. I think tensorflow upgrade their version and delete contrib. I can't find any alternative online. How can I fix this issue?

Great article and code.

Many thanks.

Just wondering though - do you have any thoughts as to how best to reduce the training time. I do not have a GPU btw.

How to Show result ? predicted price or something? not only Accuracy

My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

I know this isn't really an issue, but I thought this might be the best way to ask the question. Can anyone point me to a good tutorial for writing something to use the keras checkpoint file this outputs in order get a prediction?

Thanks!