Technical Analysis Library using Pandas and Numpy
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Updated
May 23, 2024 - Jupyter Notebook
Technical Analysis Library using Pandas and Numpy
🐬 Feature-rich, stable and customizable Flipper firmware
Simulink-based whole body controllers for humanoid robots.
Simple javascript library containing methods for financial technical analysis
Andreas Clenow - Stocks on the Move
Short description for quick search
MVC pattern for flutter. Works as state management, dependency injection and service locator.
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent
Visualizing Gradient Descent with Momentum in Python
Generate trading orders (BUY, SELL, NOTHING) using technical orders' rules based on historical data. Manual Strategy uses my modest intuition to find the optimal policy. QLearnerStrategy uses Machine Learning (QLearning algorithm) to find the optimal policy: four indicators are discretized to generate each state.
C# TA library for real-time financial analysis, offering ~100 indicators. Available on NuGet, Quantower compatible. Ensures early validity of calculated data, calculation accuracy tested against four TA libraries.
Financial indicators for use with Data-Forge
🤖 bare-bones implementation of a neural network with numpy
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Week 1 assignment form Coursera's "Advanced Machine Learning - Introduction to Deep Learning"
Add a description, image, and links to the momentum topic page so that developers can more easily learn about it.
To associate your repository with the momentum topic, visit your repo's landing page and select "manage topics."