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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Predict operation stocks points (buy-sell) with past technical patterns, and powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM..Real time Twitter:
| Date | Stars |
|---|---|
| 2026-07-24 | 313 |
| 2026-07-25 | 313 |
| 2026-07-28 | 313 |
| 2026-07-30 | 313 |
| 2026-07-31 | 313 |
| 2026-08-06 | 313 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
If you have problems with installation, let me know.
I am searching _**collaborators for this project**_. If you have experience and want to collaborate We are currently developing privately, if you want to join the team please contact us.
**Here the result: https://tuisku.eu/**
_**SEE ALSO**_ [Machine Learning Strategy](https://github.com/Leci37/Strategy-stock-Random-Forest-ML-sklearn-TraderView/tree/main) <img src="https://github.com/Leci37/ML-Sklearn-strategy-stock-crypto-for-TraderView/blob/main/img/give_a_start-RB.png" alt="give_a_start.jpg" width="30" />
https://www.linkedin.com/in/luislcastillo/
### Why this stock prediction project ?
Things this project **offers** that I did not find in other free projects, are:
+ Testing with _**36 models**_. Multiple combinations features and multiple selections of models, easily expandable (TensorFlow , XGBoost, Sklearn, LSTM, GRU, dense, LINEAR etc )
+ Threshold and quality _**models evaluation**_
+ Use _**637**_ technical stocks indicators
+ Independent neural network selection of the best technical patterns for each stock
+ Response _**categorical target**_ (do buy, do sell and do nothing) simple and dynamic, instead of poor and confused, continuous target variable ("the stock will be worth 32.4 in 2 days")
+ Powerful open-market-_**real-time**_ evaluation system
+ Versatile integration with: Twitter, Telegram and Mail
+ Train Machine Learning model with _**Fresh today stock data**_
The project is long and dense, trying to install it without understanding is a mistake, the first thing to do is to run and understand the **[TUTORIAL](#tutorial)**,
To manage collaborations we have a **Telegram GROUP:**
https://t.me/+3oG6U_hp93I2M2Ix (Once executed and understood the tutorial)
(recommended to review the point: https://github.com/Leci37/stocks-prediction-Machine-learning-RealTime-telegram/edit/master/README.md#possible-improvements).
**Strategy models** with Randon Forest, simpler [Randon Forest](https://github.com/Leci37/Strategy-stock-Random-Forest-ML-sklearn-TraderView)
---
* [Why this stock prediction project ?](#why-this-stock-prediction-project--)
+ [AUTHOR'S LICENSE:](#author-s-license-)
- [**TUTORIAL**](#tutorial)
- [IMPORTANT: Once executed and understood, join the community to avoid repeating work or try out useless developments already done.](#important--once-executed-and-understood--join-the-community-to-avoid-repeating-work-or-try-out-useless-developments-already-done)
* [INTRODUCTION](#introduction)
+ [Self-fulfilling prophecy principle](#self-fulfilling-prophecy-principle)
+ [Ground True is the variable `buy_seel_point`](#ground-true-is-the-variable--buy-seel-point-)
* [Quick start-up Run your own models](#quick-start-up-run-your-own-models)
- [**Detailed start-up**](#detailed-start-up)
+ [1 Historical data collection](#1-historical-data-collection)
- [**1.0** (Recommended) alphavantage API](#--10----recommended--alphavantage-api)
- [**1.1** The OHLCV history of the stock must be generated.](#--11---the-ohlcv-history-of-the-stock-must-be-generated)
+ [2 Filtering technical indicators (automatically by default)](#2-filtering-technical-indicators--automatically-by-default-)
+ [3 Training TensorFlow XGB and Sklearn](#3-trian-tensorflow-xgb-and-sklearn)
+ [4 Evaluate quality of predictive models](#4-evaluate-quality-of-predictive-models)
+ [5 Predictions](#5-predictions)
- [**5.0** make predictions of the last week Optional Test](#--50---make-predictions-of-the-last-week-optional-test)
- [**5.1** Getting OHLCV data in real time](#--51---getting-ohlcv-data-in-real-time)
- [**5.2** Setting up chatIDs and tokens in Telegram](#--52---setting-up-chatids-and-tokens-in-telegram)
- [**5.3** Sending real-time alerts Telegram](#--53---sending-real-time-alerts-telegram)
- [**Possible improvements:**](#--possible-improvements---)
- [Combine the power of the 17 models](#combExcerpt of 69,443 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:968c63cdf2f90cda, topic:deep-learning, topic:tensorflow