Sebastián García Astorga's starred repositories
curso-ia-generativa-y-llms
Un curso para meterse en todo lo que es Generative AI y modelos grandes de lenguaje (LLMs) con roadmaps y notebooks para Colab.
OpenExchange
Open source cryptocurrency exchange
FinanceOps
Research in investment finance with Python Notebooks
quantitative-equity-portfolio-management
On this repositery you will find several algorithms we implemented for a university research project. We hold no responsability as regards the use you may have of these algorithms.
EquityVision
Stock Forecast Research
streaming-stock-sentiment-tool-Strocks
Stock trading tool using real-time analytics from social media (Twitter)
Stock-Prediction-DL
Using historical financial data and aggregated social media sentiment from Twitter to determine the stock signal (Buy or Sell) for a given trading day. Comparing the performance of deep learning models such as LSTMs, GRUs, and RNNs when performing stock signal prediction with and without social media sentiments
stocks2mars
Social media trading bot based on comments from influencers
Hedge-Fund-stock-market-analysis
Developed a deep learning model that allows trading firms to analyze large patterns of stock market data and look for possible permutations to increase returns and reduce risk. Trained the model using a Multilayer Perceptron Neural Network on a vast set of features that influence the stock market indices. Performed technical analysis using historical stock prices and fundamental analysis using social media dat
AIStockTradingBot
WARNING: using this software will probably lose you money. The AIStockTradingBot will someday handle my Stock Trading Strategy while I am away. The intention is to handling trades and save/analyzing price data based on algorithms. Also providing actionable notifications. Currently this is all based on the TD Ameritrade API but the long term intention is to provide this service to multiple account providers. Also news and social media AI updates are very desired in time.
Relating-Social-Media-to-Stock-Movement-Public
We explore the application of Machine Learning for predicting the return of the stock by using the information of stock returns. A trading strategy based on this analysis leads to increased trading profits up to three times compared with a simple buy and hold strategy.
NLP-Trading
Twitter Bot that uses NLP to predict the sentiment of Trump's tweets on the stock market
archimedes-1
Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
newsToPriceCorrelator
This program automatically correlates the frequency and appearence of certain words and phrases with price movements of relavent coins to be used to predict future prices of that coin.
twitter_sentiment
Analyze Twitter tweets for sentiment of two exchange traded funds, EEM and HYG
Stock-Prediction-With-Twitter
This project tweets day-trading predictions for stocks. Tweet the account and receive a response on what the stock's prediction is for tomorrow.
FeelsTrader
A trading algorithm and website based off of public market sentiment. The project stems from the idea of predicting stock market sentiment via deep learning neural networks based off of social media platforms such as Twitter, StockTwits, Reddit, etc. A model trained on sentiment-labeled tweets/messages will be able to predict sentiments of tweets/messages posted in the future, allowing for the information to make trading decisions. These predictive results will also be visually displayed on the Django web application for users to make their own trading decisions as well.
Predict-Cryptocurrency-Trading-with-Twitter-Data
Predict Bitcoin Trading Trend using Tweets from Twitter
Stock-Tradings
twitter scraper, pattern detection, option trajectory, etc.
Strategist
Develop trading strategy and employ sentiment analysis on Twitter/Reddit posts
sentiment-analysis-twitter
Created trading strategies for investors based on sentiment analysis
NLP-twitter-stock-trader
Stock trading using NLP sentiment analysis
TwitterTrader
A simple stocks trading bot that trades based on trending tweets!