pythonforfinance
There are 1 repository under pythonforfinance topic.
- Fin-Maestro-Web
devfinwiz / Fin-Maestro-Web
Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.
options-tradingstock-analysistrading-strategiesvaluation-modelbacktesterbacktesting-trading-strategiespythonforfinancesentiment-analysiscryptoanalysisfundamental-analysisscreenersswot-analysistechnical-analysisquantitative-financestock-markettrading-systemsalgorithmic-tradinggradiostock-price-predictionmachine-learningLanguage:Python 227 SharmaVidhiHaresh / Backtesting-Trading-Strategies-with-Python
In this project, I had backtested the cross-over trading strategy on Google Stock from Jan 2016 to June 2020. By using historical time-series data, I had tested the Moving Average(MA) cross-over strategy and Relative Strength Index (RSI) strategy with a stop loss at a price that closes 2% or more below 10-day MA. I had plotted the equity curve with drawdowns and P&L, as well as volume, relative strength index (RSI), stock pricing chart and simple moving averages.
Language:Jupyter Notebook 40- Pynaissance
conquerv0 / Pynaissance
A walk through the frameworks of Python in Finance. The repository is currently in the development phase. The finalized version will include a full-fledged integration and utilization of Quantopian, GS-Quant, WRDS API and their relevant datasets and analytics.
Language:Jupyter Notebook 25 Using a dataset of hedge fund indices, I had computed various risk parameters, explicitly Value at risk (VaR), drawdown and deviation from normality with Python. Using different models, I had computed non-parametric VaR, Parametric Gaussian Model VaR and Cornish-Fisher VaR, as well as plotted the VaR of all hedge fund indices.
Language:Jupyter Notebook 18The concentration of five tech giants, namely Facebook, Amazon, Microsoft, Apple and Google in the S&P500 have increased significantly. It is a common view on the street that the concentration level has surpassed that during the Dot-com bubble in 1999. Thus, extracting the time series data set for cumulative stock price of the above tech stocks and S&P500, we would compute and plot the cumulative daily returns on tech stocks that are driving the raging market, as well as the S&P500. Then, we would compute the breadth between the major tech stocks and S&P500.
Language:Jupyter Notebook 2- Language:Jupyter Notebook
