News Sentiment & Reinforcement Learning in Finance & Algorithmic Trading

This is the recording of the 1st Cross-Meetup-Group Virtual Event. General slides are found under https://hilpisch.com/virtual_meetup_01.pdf.

Talks are:

Dr. Richard L. Peterson & Anthony Luciani (MarketPsych Indices):
Creating Market Forecasts with News and Social Media Data using Jupyter Notebooks
Slides: https://go.aws/2yBjF4u

Dr. Yves Hilpisch (The Python Quants | The AI Machine):
Reinforcement Learning: From Playing Games to Trading Stocks
Slides: https://hilpisch.com/rlearn_finance.pdf
Notebook: https://hilpisch.com/rlearn_finance.html

The event is co-organized by The Python Quants and Refinitiv.

Quantlab is that Machine Learning cont. randomurl. Timeseries

Quantlab - Machine Learning cont. w. Timeseries

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.
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Demand forecasting and inventory planning software demonstration
https://gmdhsoftware.com/

Training information: Profound Strength Learning For Computerized Dealing Python

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

Telegram Group: https://t.me/joinchat/DmGkrhIE_g6Mk-zJS6sWgA

Links:
OpenAI Gym: https://gym.openai.com/
BTGym: https://github.com/Kismuz/btgym
backtrader: https://www.backtrader.com/
TensorForce: https://github.com/reinforceio/tensorforce
Bitcoin TensorForce Trading Bot: https://github.com/lefnire/tforce_btc_trader
Self Learning Quant: https://hackernoon.com/the-self-learning-quant-d3329fcc9915
DQN: https://towardsdatascience.com/reinforcement-learning-w-keras-openai-dqns-1eed3a5338c

T06 – Missing out on beliefs system Insight in Tamil how to Tool learning course free ( Statistics Technology )

T06 - Missing values strategy Intuition in Tamil - Machine learning course free ( Data Science )

*For the playlist , please click the below Link:

*Please click the following link to download the dataset: https://drive.google.com/file/d/10DbrdE0RTG8KMkdiOPu-7r1cgBOfhcyi/view?usp=sharing

*Visit Our website : https://datasciencealive.wordpress.com/machine-learning/

*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

*Data Preprocessing
*Supervised Learning – Algorithm
*Classification
*Regression
*Association
*Unsupervised learning – Algorithm
*Clustering
*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 .

*For the playlist , please click the below Link:

#Data_science_tamil #Missing_values #Machine_learning_Python_tamil

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.

Machine learning neural internet inquiries facing straight-forward imperative transaction like the masters

Another follow from a newsletter subscriber
http://quantlabs.net/blog/knowledgebase/machine-learning-neural-net-questions-vs-simple-fundamental-trading-like-the-pros/
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New Next Generation Neural Network Trading Software | Gabriel Brent

-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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