StockTrader

Point of stock trader is to learn about trading stocks and how the stock market works

Library

1. quandl 3.3.0
2. matplotlib 2.1.1
3. numpy 1.14.0
4. fbprophet 0.2.1
5. pystan 2.17.0.0
6. pandas 0.22.0
7. pytrends 4.3.0
8. pip install finance
9. pip install qfrm
10. pip install plotly
11. pip install pandas-datareader
12. pip install beautifulsoup4
13. pip install sklearn
14. pip install pyfin
15. pip install scrap-ticker-symbols
16. pip install wallstreet
17. pip install alphalens
18. pip install pandas-finance

Need to install

1. pip install afterhours
2. pip install zipline

Doc links

1. https://github.com/addisonlynch/iexfinance
2. https://www.alphavantage.co/documentation/
3. https://api.tiingo.com/docs/general/overview
4. https://iextrading.com/developer/docs/#usage
5. http://docs.enigma.com/public/public_v20_api_about.html
6. https://github.com/datawrestler/after-hours
7. http://www.zipline.io/
8. http://www.bruunisejs.dk/PythonHacks/

Pandas-dataReader

Remote data access for pandas, access to data from Robinhood, Alpha Vantage, Enigma, Quandl, TSP

Pandas Information

Matplotlib Information

1. .rolling
2. subplot2grid - READUP on how this works

Basic Terms

1. Call Options -
2. Put Options -
3. Open - Price of one share when the stock market opens
4. High - Highest value of stock throughout the day
5. Low - Lowest value of stock throughout the day
6. Close - Final price of stock when the market closes
7. Volume - How many shares were traded
8. Adjusted Close - Stock split
9. Moving Averages - take current prices and prices from x days add them up divide by x days

Categories of Machine Learning Algorithms

1. Supervised - You go to war and dont stop until all enemies are destoryed
   • Linear Regression, Logistic Regression, Decision Tree, Random Forest
2. unsupervised - Look at your strength and weaknesses and decide if it fesible to fight
   • k-means, apriori
3. Reinforcement - You have entered the fight and accessing your position. Are you losing men? And you act according
   • markov decision process

Machine Learning Algorithms

1. Dataset - View as your opponent
2. Linear Regression - completely taking over your opposition, the point of no return
3. Logistic Regression - taking simplistic assumptions and then deciding whether to fight or not
4. Tree Based Modeling - Divide and Rule, you divide your opponents with smart strategy and take them over
5. Bayesian Modeling - probability of winning in different battle base types such as air, land and sea, act accordingly based off the information
6. Support Vector Machines - Drawing out territory and boundary where your advantage on field base. Soldiers might be adept to fighting in a specific area.
7. k nearest neighbor - checking past outcomes and mapping accordingly, evaluate your performance in past battles, contemplate on your weak and strong areas and prepare for the next fight
8. k-means - Building up alliances with provinces which share the same philosophy, goals and motvies, trying to be more powerful then ever before
9. Neural Network and Preceptrons - Every soldier in your army decides whom to fight
10. Ensemble Model -
11. Anomaly Detection - checking for unusual patterns in your own army. You might have secret agent amongst your team