# Is this Python code of linear regression really machine learning

Is this Python code of linear regression really machine learning? Seriously, why do less knowledgable people just rely on the result of some popular machine learning framework like TensorFlow. Don’t you think it is wise to understand the underlying math? I have used this stuff with MATLAB well before the terms big data and machine learning  became popular. I am no expert here but I would like to have some experts add their opinion on it.

## Looking for input

Comment away in my video where I am wrong. I like to learn what you think. All I ask is be respectful about it

Robert Pardo book for forward walking

https://onlinelibrary.wiley.com/doi/book/10.1002/9781119196969

https://en.wikipedia.org/wiki/Linear_function

TensorFlow and Nutonian machine learning for algo trading tips

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# Bayesian Linear Regression in Python: Using Machine Learning. Is R code walkthrough better?

If you are into machine learning, this is one popular technique used for forecasting. Actually, using Bayesian appears to be standard. Many people have like this posting which is an entire tutorial on how to accomplish this task. Here is the article for you:

## Is R better

This particular totorial is completed in Python but R could be somewhat useful in other instances.

Lastly, I am quite surprised on how well my old legacy R course was quite popular. In case you missed it, here are the details:

I just posted this old legacy R course if you are interested. This include R code walkthrough. I have many posted on this language here

Purchase here if interested

To be quite honest, I am quite surprised on how popular this seems to be among my site visitors.

Note that this is older version of R using version 2.15!

Here are the details with a video at this location

R Course with Technical Analysis

R Course with Technical Analysis

Module 1

## Technical Analysis in R

Technical Analysis in R

Unit 1

30 day moving average function

Unit 2

2 sided moving average for mean rolling window

Unit 3

R Code Walkthrough Improved Moving Average using intra day for Forex data

Unit 4

The improved moving average

Unit 5

R Code Wakthrough Simple Moving Averag Strategy with Volatility Filter

Unit 6

Love level Improved Moving Average functions with testing code

Unit 7

R source code for trading script with update portfolio, position size, MA, cross over, SMA, optimize parameters pt 2

Unit 8

R source code for trading script including MACD, Omega performance, RSI, and Bollinger Band measuring strategy and portfolio performance with plots Pt 3

R Course with Quant including GARCH

Module 1

Unit 1

Walthrough Parallel R Model Prediction Building and Analytics

Unit 2

Intro to GARCH forecasting with various R packages

Unit 3

How to use GARCH for predict market movements

Unit 4

How to use GARCH to predict distributions

Unit 5

## GARCH trading R script walkthrough with a rolling window

R Course with Quant

R Course with Quant

Module 1

Intro

Intro

Unit 2

An ARMA model R code walkthrough

Unit 3

Checklist of forecasting with ARIMA: is time series stationary, differentiate, ARIMA(p,d,q), and which AMRA model to use?

Unit 4

R code walkthrough: Detrend to use Auto ARIMA modelling and forecast with statistical data and Ljung BoxTest

Unit 5

My first version of ARIMA R script with Forex data and Equity 1 and 5 min frequency

Unit 6

Bayesian analysis to Compare algorithms with Gibbs

Unit 7

Markov Chain R source code walkthrough

Unit 8

Monte Carlo R Walkthrough Demo

Unit 9

An alternative to running a Monte Carlo simulation

Unit 10

R code walkthrough Mean Absolute Deviation with Efficiency Frontiers Demo

R Course with Mean Reversion and Pair Trading

Module 1

## Mean Reversion in R

Mean Reversion in R

Unit 1

Backtesting a Strategy with Mean Reversion

Unit 2

Mean Reversion Euler with Ornstein Uhlenbeck process

Unit 3

Pairs trading R source code walkthrough with mean reverting logic, spread and beta calculation

Module 2

Unit 1

Poor mans Pair Trading with Cointegration R Walkthrough

Unit 2

Pair trading with S&P 500 companies

Unit 3

Unit 4

Unit 5

Unit 6

Unit 7

Pairs trading with a Hedge Ratio Demo

Unit 8

R Code Walkthrough Back testing with trading pair with CAPM

Unit 9

Gold versus Fear in Cointegration test

## R Course with Arbitrage and Volatility

Arbitrage and Volatility

Module 1

## Arbitrage in R

Arbitrage in R

Unit 1

Beating a random walk with arbitrage

Unit 2

Beating a random walk with arbitrage

Unit 3

Time Based Arbitrage Opportunities in Tick Data: Why low latency is needed in HFT?

Unit 4

Building a currency graph with arbitrage

Unit 5

Arbitrage: Modelling returns with CAPM APT aka Abritrage Pricing Theory

Unit 6

Indian equity market index NIFTY anaysis with CAPM vs APT aribitrage pricing theory using PCA and moment analysis

Module 2

## Volatility in R

Volatility in R

Unit 1

R Code Walkthrough Adding a volatility filter with VIX

Unit 2

R Code Wakthrough Simple Moving Averag Strategy with Volatility Filter

Unit 3

Mean Reverting with Volatility Spike

Unit 4

Trading with GARCH volatility R script walkthrough demo

Unit 5

Jeff Augen volatility spike code

Reminder from yesterday. This closes out TONIGHT as well:

This is the your chance to learn about behind the scenes of these trading patterns I presented on Monday night. I have made this replay video now private which means it is only available to my Quant Elite Members. This is a very limited and exclusive offering to access it! I even revealed the source of how the Python code was created to generate them, This will never be seen again after this Friday! It is way too valuable that I don’t need the world to know how these work.

This posting will be removed this Friday night Eastern Standard Time (same as NYC)!

I Want To Learn Trading Patterns Now

Please find the upcoming new items I will be adding the next few months for this membership:

1. Live Q&A workshop bootcamp for the Python 3 Infrastructure Course for a Primitive Algo/Automtated Trading System

2. Packaged up course of using Dukascopy JForex API for automated forex and CFD trading. (this is partially done now with access for this membership)

3. Daily charting within the Quant Analytics service.

Here are the details with benefits of this trial membership

REMEMBER: My patterns talk will be removed forever as of Friday!!!

Always remember I just created the online store for all other product and services.

I Want to Learn Trading Patterns Now

Please find the upcoming new items I will be adding the next few months for this membership:

1. Live Q&A workshop bootcamp for the Python 3 Infrastructure Course for a Primitive Algo/Automtated Trading System

2. Packaged up course of using Dukascopy JForex API for automated forex and CFD trading. (this is partially done now with access for this membership)

3. Daily charting within the Quant Analytics service.

Here are the details with benefits of this trial membership

RMEMEBER: My patterns talk will be removed forever as of Friday!!!

Always remember I just created the online store for all other product and services.

P.S. Let me know if you are interested in an annual term as well.

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# tensorflow machine learning standard option for algo trading but there is simpler options

tensorflow machine learning standard option for algo trading but there is simpler options

Various links below show simple ways to use linear regression with R Squared. It then evolves into using SciKitLean Python Package which is relatively simple but could be effective if you are just after R Square to measure strength of the trend.

towardsdatascience.com/simple-and-multiple-linear-regression-in-python-c928425168f9

blastchart.com/Community/IndicatorGuide/Indicators/LinearRegressionRSquared.aspx

This next link offers a look the complicated TensorFlow Model Library from Google. This is the definite standard defacto in the financial industry. I know this when I saw samples used by high end HFT shops with their workshops for the Newsweek AI conference in NYC last Nov.

Set stock data target with https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 <– TensorFlow complex but offers many options

I will also say that TensorFlow offer the most choice. See below a tip below from someone in my private Telegram group. This could save you loads of time instead of wasting time by going down rabbit holes.

For crypto use, https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/

Note that this uses Keras which obviously simpler than TensorFlow. I would probably move towards TF as I get deeper into this.

## Here is that useful tip:

My quick-fire tips: in general I’ve had more success constantly-retraining the ML models (every day in your case) as opposed to the usual train/test/validate method. Either that, or use ML models that are specifically designed to capture the time-varying properties of time series (these models can usually be updated in an online fashion). Never used raw prices as inputs—there is so much autocorrelation in the features that the predicted price will just end up being the last close. Instead use returns (I prefer logarithmic returns over simple returns, but it doesn’t make much difference).

Try to use a target that is directly related to the outcome of a potential trade. If that is too complicated, try having separate targets for direction and volatility. I prefer regression algos to classifiers. If possible, require that your model predicts opposite scenarios for both a long and short trade; e.g. it predicts a long trade will make money *AND* a short trade will lose money, and vice-versa. Avoid neural networks when you’re just starting out – there are too many hyperparameters to tune. Ensemble methods are fantastic, and can be very profitable

## Here is my conclusion:

If you are like me just trying to figure out general trend through LSR and R-square, I tend to use these for now. As a result, I may use the SciKitLearn example for simplicity but stop there. I really don’t want to go down the various rabbit holes to experiment with more complex ML libraries. I see no reason compared to the recent sub minute study/reseach/playing around experiment at the sub minute level I started back in Dec which became a time waster with no results.

For those keeping track, if you don’t want to use Python with TensorFlow there is a simpler C++ option

https://tebesu.github.io/posts/Training-a-TensorFlow-graph-in-C++-API

This is the simplest so far but which started this whole level of research

https://stackoverflow.com/questions/893657/how-do-i-calculate-r-squared-using-python-and-numpy

You can see how simple stuff turns complex

Williams % and Stochastics most reliable Matlab technical indicator

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# TensorFlow and Nutonian machine learning for algo trading tips

TensorFlow and Nutonian machine learning for algo trading tips

Here they are for curve fitting or pattern recognition

Pro Deep Learning with TensorFlow – pdf – Free IT eBooks Download

http://www.allitebooks.com/pro-deep-learning-with-tensorflow/

http://www.allitebooks.com/learning-tensorflow/

https://www.nutonian.com/ <- #1 tool for trading used by pros

http://chaoshunter.com/ <– would you trust this ??

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# Gold Price Forecasting Using Python Machine Learning

 Gold Price Prediction Using Python Machine Learning This is a basic way to apply machine learning Python packages like SciKit Learn. As gold is now becoming the number 1 asset of interest, here is a way to analyze movements on various gold ETFs. This is nothing revolutionary but it gives you the high level on how to implement these type of analysis and forecasting on popular market assets of the time Links here http://quantlabs.net/blog/2018/01/gold-price-prediction-using-python-machine-learning/   Also, I have posted a crucial item on understanding the bid and ask spread. If you never get the fundamentals on this, you will pretty well be failing with no knowledge why. Knowing this will help my forex strategy analysis much more. I will be posting a new video on this pointing back to this article. Let me know if you are interested in this by responding. Thanks Bryan Gold Price Prediction Using Python Machine Learning http://quantlabs.net/blog/2018/01/bitcoin-price-forecasting-using-monte-carlo-simulation-forex-analysis/
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# Gold Price Prediction Using Python Machine Learning

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Gold Price Prediction Using Python Machine Learning

This is a basic way to apply machine learning Python packages like SciKit Learn. As gold is now becoming the number asset of interest, here is a way to analyze movements on various gold ETFs. This is nothing revolutionary but it gives you the high level on how to implement these type of analysis and forecasting on popular market assets of the time.

Gold Price Prediction Using Machine Learning In Python

Bitcoin and Doge crypto currency the joke or still serious ?

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

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# Google Machine Learning TPU Chips Are Faster Than Nvidia GPUs

Google Machine Learning TPU Chips Are Faster Than Nvidia GPUs

This could get interesting leveraging off of Google Cloud

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# Zero-Math Intro Reference to Markov Chain Monte Carlo Machine Learning Approximating Method

 Zero-Math Intro Reference to Markov Chain Monte Carlo Machine Learning Approximating Methods Non complicated formulas to look at in this article
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# Zero-Math Intro Reference to Markov Chain Monte Carlo Machine Learning Approximating Methods

Zero-Math Intro Reference to Markov Chain Monte Carlo Machine Learning Approximating Methods

https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50

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# Fintech Startup Uses Quantum Computing to Boost Machine Learning

The cloud is an option here to for this quantum computing

https://www.technologyreview.com/s/609804/a-startup-uses-quantum-computing-to-boost-machine-learning/amp/

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