This is the recording of the 1st Cross-Meetup-Group Virtual Event. General slides are found under https://hilpisch.com/virtual_meetup_01.pdf.
Dr. Richard L. Peterson & Anthony Luciani (MarketPsych Indices):
Creating Market Forecasts with News and Social Media Data using Jupyter Notebooks
Dr. Yves Hilpisch (The Python Quants | The AI Machine):
Reinforcement Learning: From Playing Games to Trading Stocks
The event is co-organized by The Python Quants and Refinitiv.
A continuation of the previous Machine Learning example. Here we use our Neural Net code on historical financial timeseries to get a prediction of the future in our target exchange rate. Note that this is just an example of using the Neural Net for timeseries and not an actual predictor of the exchange rate. Video Rating: / 5
Demand forecasting and inventory planning software demonstration
In this tutorial, we’ll see an example of deep reinforcement learning for algorithmic trading using BTGym (OpenAI Gym environment API for backtrader backtesting library) and a DQN algorithm from a medium post (link below) to interact with the environment and does the trading.
Access to the code: https://gist.github.com/arsalanaf/d10e0c9e2422dba94c91e478831acb12
*In this session we will look into strategy used in Handling missing values on the data preprocessing techniques ( Tamil ) using pandas in python .
We will use the following strategy for handling the missing values
1. Mean = Average
2.Median = Middle value
3.Mode ( categorical data and continues values) = Most Frequent values
In machine learning ( Tamil ) most of the time will be spend on data preprocessing , data mining and feature extraction . Hence please listen to this topic more carefully .
*This is a Data science course ( Tamil ). This is a full fledge course for free and we will cove all the main topics on the machine learning algorithm. This course is specifically designed to address all the queries from beginners to expert . Artificial intelligence ( AI ) is a bigger umbrella ,In that Machine learning ( ML ) and Deep Learning ( DL ) are part of Artificial Intelligence.
*In this video we will have an overview on the topics that will be covered. On high level it will be
*Supervised Learning – Algorithm
*Unsupervised learning – Algorithm
*Dimensionality Reduction (PCA)
*Semi -Supervised learning
*Re- Enforcement learning
*Best approach for Model selection
*Intro to Deep Learning
The above topics will be covered in-detail on the upcoming session which you can find it in the below playlist .
Link to the code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Time_Series/Part1_Time_Series_Data_BasicPlotting.ipynb
Viewing Pandas DataFrame, Adding Columns in Pandas, Plotting Two Pandas Columns, Sampling Using Pandas, Rolling mean in Pandas (Smoothing), Subplots, Plotting against Date (numpy.datetime), Filtering DataFrame in Pandas, Simple Joins, and Linear Regression.
This tutorial is mostly focused on manipulating time series data in the Pandas Python Library.
Another follow from a newsletter subscriber
http://quantlabs.net/blog/knowledgebase/machine-learning-neural-net-questions-vs-simple-fundamental-trading-like-the-pros/ Video Rating: / 5
-Where to put your stops and Targets based on Price Action and support / resistance visualization
-When to place your trade base on historical price action turning points
-Why to place a trade using our smart neural network trend following software
-How to Predict the Future of price using real support resistance and supply demand concepts
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