文件列表:

Documents (0, 2019-03-27)
Documents\SMAIProjectAbstract.pdf (102446, 2019-03-27)
Documents\StockPricePrediction.pdf (665338, 2019-03-27)
LICENSE (1083, 2019-03-27)
Report.pdf (136581, 2019-03-27)
input (0, 2019-03-27)
input\params.txt (1502, 2019-03-27)
input\symbols.txt (8, 2019-03-27)
requirements.txt (162, 2019-03-27)
screenshots (0, 2019-03-27)
screenshots\presentation.gif (5455451, 2019-03-27)
scripts (0, 2019-03-27)
scripts\Algorithms (0, 2019-03-27)
scripts\Algorithms\LSTN-RNN.py (3947, 2019-03-27)
scripts\Algorithms\Neural_Network.py (16336, 2019-03-27)
scripts\Algorithms\regression_helpers.py (8744, 2019-03-27)
scripts\Algorithms\regression_models.py (2849, 2019-03-27)
scripts\Algorithms\rnn_lstm.py (4405, 2019-03-27)
scripts\Algorithms\svm.py (2580, 2019-03-27)
scripts\Stock-Prediction-Copy1.ipynb (878439, 2019-03-27)
scripts\Stock-Prediction.ipynb (1067999, 2019-03-27)
scripts\add_s_and_p_index.py (1404, 2019-03-27)
scripts\feature_selection.py (1669, 2019-03-27)
scripts\fetch_stock_data.py (2923, 2019-03-27)
scripts\interpolation.py (618, 2019-03-27)
scripts\main.py (749, 2019-03-27)
scripts\normalization.py (313, 2019-03-27)
scripts\preprocessing.py (2023, 2019-03-27)
scripts\twitter-sentiment-analysis (0, 2019-03-27)

# Stock Market Price Predictor using Supervised Learning ### Aim To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. ## Setup Instructions ``` $ workon myvirtualenv [Optional] $ pip install -r requirements.txt $ python scripts/Algorithms/regression_models.py ``` Download the Dataset needed for running the code from [here](https://drive.google.com/open?id=0B2lCmt16L_r3SUtrTjBlRHk3d1E). ## Project Concept Video [](https://www.youtube.com/watch?v=z6U0OKGrhy0) ### Methodology 1. Preprocessing and Cleaning 2. Feature Extraction 3. Twitter Sentiment Analysis and Score 4. Data Normalization 5. Analysis of various supervised learning methods 6. Conclusions ### Research Paper - [Machine Learning in Stock Price Trend Forecasting. Yuqing Dai, Yuning Zhang](http://cs229.stanford.edu/proj2013/DaiZhang-MachineLearningInStockPriceTrendForecasting.pdf) - [Stock Market Forecasting Using Machine Learning Algorithms. Shunrong Shen, Haomiao Jiang. Department of Electrical Engineering. Stanford University](http://cs229.stanford.edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms.pdf) - [How can machine learning help stock investment?, Xin Guo](http://cs229.stanford.edu/proj2015/009_report.pdf) ### Datasets used 1. http://www.n***aq.com/ 2. https://in.finance.yahoo.com 3. https://www.google.com/finance ### Useful Links - **Slides**: http://www.slideshare.net/SharvilKatariya/stock-price-trend-forecasting-using-supervised-learning - **Video**: https://www.youtube.com/watch?v=z6U0OKGrhy0 - **Report**: https://github.com/scorpionhiccup/StockPricePrediction/blob/master/Report.pdf ### References - [Scikit-Learn](http://scikit-learn.org/stable/) - [Theano](http://deeplearning.net/software/theano/) - [Recurrent Neural Networks - LSTM Models](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) - [ARIMA Models](http://people.duke.edu/~rnau/411arim.htm) - https://github.com/dv-lebedev/google-quote-downloader - [Book Value](http://www.investopedia.com/terms/b/bookvalue.asp) - http://www.investopedia.com/articles/basics/09/simplified-measuring-interpreting-volatility.asp - [Volatility](http://www.stock-options-made-easy.com/volatility-index.html) - https://github.com/dzitkowskik/StockPredictionRNN

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