StockRecommendSystem:用于股票分析，预测和交易的智能推荐系统

# StockRecommendSystem ## Main Requirement: Python 3.5.2 Keras 2.0.6 TensorFlow 1.2 pymongo tqdm nltk googletrans <br> ## Install brew install mongodb --with-openssl brew services start mongodb mongod --dbpath (Your Porject Folder)/Data/DB When you storing stock data with mongodb mode, you may meet [too many open files](https://superuser.com/questions/433746/is-there-a-fix-for-the-too-many-open-files-in-system-error-on-os-x-10-7-1) problem, try the following codes in command line: > sysctl -w kern.maxfiles=20480 (or whatever number you choose) > sysctl -w kern.maxfilesperproc=18000 (or whatever number you choose) > launchctl limit maxfiles 1000000 (or whatever number you choose) > brew services restart mongodb > mongodump -h localhost:27017 -d DB_STOCK -o ./ <br> ## Data Fetching: Cover stock related data fetching, storaging in either MongoDB or CSV mode (See config.ini [Setting] sector for more detail). 1. Stock:（NSDQ, NYSE）-> US, (HKSE) -> HK, (SSE，SZSE) -> CHN 2. Earning: US stock market earning info. 3. Short: US stock market short squeeze info. (Require Multi IP Routing Support) 4. News: NewsRiver 5. Media: Twitter Data ### Data Structure ** US Stock List ** DB : DB_STOCK SHEET: SHEET_US_DAILY_LIST ITEM : symbol, name, market_cap, sector, industry, stock_update, news_update ** US Stock Daily ** DB : DB_STOCK SHEET: SHEET_US_DAILY_DATA ITEM : symbol (stock symbol) data -> [{date, open, high, low, close, adj_close, volume}] ** US Stock Earning ** DB : DB_STOCK SHEET: SHEET_US_EARN ITEM : symbol （date） data -> [{date, symbol, analyist, estimate, actual, surprise}] ** US News ** DB : DB_STOCK SHEET: SHEET_US_NEWS ITEM : symbol, date, time, title, source, ranking, sentiment, uri, url, body_html, body_eng, body_chn ### Run cd Source/FetchData python Fetch_Data_Stock_US_Daily.py <br> ## Stock Prediction: Under Development... <br> ## Stock Processing: Correlation > Company1 Company2 Correlation > QQQ TQQQ 0.999 > IBB BIB 0.999 > INSE XBKS 0.999 > JAG JPT 0.999 > ACWX VXUS 0.995 > IXUS ACWX 0.993 > VONE SPY 0.992 > IXUS VXUS 0.991 > VTWO VTWV 0.988 > NTB FBK 0.988 > GOOG GOOGL 0.987 ### Run cd Source/StockProcessing python Correlation_Stock_US.py <br> ## Reinforcement Learning: This sector is directly clone from: [Link](https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading) More in mind: 1. The approach use only "Adj Close" price as input, it's supposed more features combinations shall be joined to the party. 2. The Trading Strategy is a little mediocre and limited, better rewrite it. 3. At most only two tickers are allowed in the trading system, rewrite it. testing output: > init cash: 100000 > Columns: [AMD, NVDA, SPY, ^VIX] > Index: [] > Runner: Taking action 2016-03-16 00:00:00 buy > Runner: Taking action 2016-03-17 00:00:00 buy > Runner: Taking action 2016-03-18 00:00:00 hold > ...... > Runner: Taking action 2017-06-12 00:00:00 buy > Runner: Taking action 2017-06-13 00:00:00 buy > Runner: Taking action 2017-06-14 00:00:00 buy > Final outcome: 121500.348294 ### Run cd Source/ReinforcementLearning python runner.py <br> ## ToDo: More AI approach will be arranged and upload ASAP