853 skills found · Page 11 of 29
saranshbansal / Data Science With PythonData science with Python: This repository mostly contains DataCamp data-science courses/exercises that I have completed.
thomaspurchas / Tarfile Progress ReporterA Python tarfile library wrapper that reports back percentage completes.
sachalapins / BvaluesPython notebook for calculating completeness magnitudes and b-values for earthquake catalogues
burakoner / Okx SDKUp-to-date, most-complete, well-organized, well-documented, easy-to-use OKX Exchange Rest and Websocket API SDK for Python
coveo / StewComplete Python CI/CD solution built around Poetry.
scottpetrovic / Krita Python Auto CompleteA python script that can grab the Krita source code and generate auto-complete files
jwillis0720 / SadieThe Complete Python Antibody Library
jeffreyscheng / MewtagenPython script that helps users complete Pokemon teams in using an evolutionary algorithmic approach.
Tom25 / Hangman[NOTE: This is a *very* old project of mine, and is kept on Github for nostalgia's sake.] A simple hangman game written in Python 3.3 using a simplified version of the TKinter library called graphics.py (authored by John Zelle). This was a project I did for fun after completing a course in Python at my university.
ellenrapps / Woobu Autonomous DroneWoobu Autonomous Drone is a flying machine project. Code Completed. This project uses Raspberry Pi Pico plus MPU6050 as main hardware, and (Micro)Python, HTML and JavaScript as the main programming languages.
SOHAM-THUMMAR / Brain Tumor Detection Model Using KerasBrain-Tumor-Detection-Model-using-Keras is a Python-based deep learning project that uses the Keras (TensorFlow) framework to detect brain tumors from MRI images. It provides a complete workflow including data preprocessing, dataset splitting, model training, and saving trained models for inference or further experimentation.
ginking / Archimedes 1Archimedes 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.
PacktPublishing / Machine Learning And Data Science With Python A Complete Beginners GuideCode Repository for Machine Learning and Data Science with Python: A Complete Beginners Guide, published by Packt
bnjunge / Daraja API Complete Python Flask TutorialsThis is a simple implementation of Daraja API in Flask On every Resource consumption, check the details that need to be edited to reflect your iwn test credentials. * If you like this code sample, don't forget to star it here in Github Video Tutorials link: https://www.youtube.com/playlist?list=PLcKuwRUZRXZKfB0-5idYOo8JQKKLq_Kz-
codehariom / DSA In PythonComplete DSA in Python
anmspro / Python Turtle Graphics Beginner To AdvancedA complete overview of python turtle graphics in Bangla.
umeshpalai / Algorithmic Trading Backtesting Banknifty Straddle Using PythonPython code of complete backtest of Banknifty Option Straddle
electronicayciencia / EasyMCP2221The most complete Python library to interface with MCP2221(A).
zhudiana / FastAPI Learning Roadmap📚 A complete roadmap to mastering FastAPI: from Python fundamentals to advanced development. Includes structured notes, resources, and example projects.
HugoFara / PylinkageComplete pipeline to design, optimize and view 2D kinematic mechanisms in Python
