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Elite Financial Prediction Toolkit - Python Data Science for Stock Markets
Stock Market Data Analysis and Visualization with Python, Pandas, NumPy, Seaborn and Matplotlib
01 Fetch Stock Data (9:12)
02 Project Preview (3:17)
03 Visualize Price Distribution (9:07)
04 Calculate Daily Return (3:27)
05 Compare Returns Of Different Stocks (10:45)
06 Visualize Standard Deviation And Expected Returns (5:44)
07 Calculate Value At Risk (3:52)
08 Monte Carlo Analysis To Estimate Risk (9:11)
09 Visualize Stock Data Features (7:32)
Source Files
Build a Stock Ticker Dashboard Web App with Python, Dash and Pandas
01 Project Preview (3:00)
02 Import Stock Data (7:47)
03 Build A Dash Web App (6:19)
04 Build Stock And Date Range Pickers (10:04)
05 Show Stock Data In The Web App (14:51)
Source Files
Algorithmic Trading with Python, Statistics and Pandas - Build Investing Strategies
01 Make An API Call (6:18)
02 Project Preview (1:58)
03 Convert Data To A Pandas Dataframe (9:41)
04 Batch API Calls To Improve Performance (11:23)
05 Calculate The Number Of Shares To Buy (7:53)
06 Build An Excel File From The Pandas Dataframe (4:18)
07 Project 2 Preview (2:42)
08 Make An API Call (9:47)
09 Execute A Batch API Call (14:59)
10 Remove Low Momentum Stocks (12:12)
11 Calculate New Number Of Shares To Buy (5:30)
12 Find High Quality Momentum Stocks (11:42)
13 Calculate Momentum Percentiles (14:38)
14 Find The 50 Best Value Stocks (6:58)
15 Calculate New Number Of Shares To Buy (3:24)
16 Build An Excel File (3:41)
17 Project 3 Preview (1:55)
18 Build A Dataframe (5:58)
19 Remove Glamour Stocks (5:01)
20 Calculate The Number Of Shares To Buy (14:36)
21 Build A Composite Of Valuation Metrics (15:22)
22 Clean Dataframe (5:49)
23 Calculate Value Percentiles (4:50)
24 Find The 50 Best Momentum Stocks (8:10)
25 Calculate New Number Of Shares To Buy (5:30)
Source Files
Predict Stock Trends with Twitter Sentiment Analysis ML
01 Fetch Twitter Sentiment And Stock Prices Datasets (5:34)
02 Merge Datasets Into Dataframe (11:46)
03 Project Preview (2:27)
04 What Is The Random Forest Classifier Model (5:42)
05 Processing And Sorting Sentiment Data (14:40)
06 Process Stock And Sentiment Dataframe (5:48)
07 Calculate Stock Trend (Rising Or Falling) (8:16)
08 Build A Binary Encoding Of Sentiment (5:14)
09 Split And Scale Data (13:56)
10 Build A Random Forest Classifier Model (13:06)
11 Evaluate The Model (3:20)
12 Project Preview (1:33)
13 What Is Gradient Boosting (1:56)
14 Test Different Learning Rates (8:09)
15 Make A Prediction With A Gradient Boosting Classifier (7:13)
16 Evaluate The Model (6:48)
Source Files
Feature Analysis and Data Science with Stocks for Beginners
01 Load And Create Data (9:55)
02 Perform Exploratory Data Analysis (3:41)
03 Visualize Data With Different Plots (11:06)
04 Analyze Features With More Plots (6:17)
05 Build Plots With Seaborn (4:35)
06 Build A Bokeh Plot (6:16)
07 Build A 3D Scatter Plot (3:45)
08 Rank Feature Importance (7:13)
09 Compare Positive And Negative Returns (8:06)
10 Course Overview (7:42)
Source Files
15 Make A Prediction With A Gradient Boosting Classifier
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