- Original Repository located at: https://github.com/fredec96/Crypto_Quant_Trading
Volatile crypto’s that are correlated with BTC can return greater profits through active trading using BTC as a broad market signal. In this project, BTC is used as the most efficient cryptocurrency. By using pure technical analysis of SMA, we determine the trading signal for longing and shorting decisions. Cumulative returns are calculated to test efficiency and profitability of trading crypto's with this approach.
There is a strong correlation in the Crypto Market.
By comparing 10-day moving average with 20-day moving average, long and short signals are determined.
- Purple arrows: exit short trade and execute long.
- Orange arrows: exit long trade and execute short.
Monero (XMR) and Ethereum (ETH) provide greater returns than Bitcoin and yield 141.8271x and 132.0413x respectively
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pandas - For data analysis
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pathlib - For reading file paths
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glob - To iterate over multiple file paths
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plotly - To create interactive plots
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numpy - For scientific computing
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hvPlot - To create interactive plots
Historical Price Data Collected from: https://www.cryptodatadownload.com/data/bitfinex/
Before running the application first install the following dependencies.
Plotly
Use the package manager pip to install Plotly:
pip install plotly==5.10.0hvPlot
Use the package manager pip to install hvPlot:
pip install hvplotTo run the Crypto_Quant_Trading analysis files you must first clone the repository to your local machine:
git clone <paste link here>- Data collection, cleaning, and concatenation was done in the
bitfinex_data_collection.ipynbfile - For ease of use the cleaned and concatenated CSV files have already been saved in the repository as
Bitcoin_Data.csvandCoins_Data_Master.csv - All of the original CSV files are located in the
Raw_CSVbranch
Open the data_analysis.ipynb file and run to view the data analysis and graphics
Abhir Mehra
Cole Frederick
Josh Thompkins
Rebekah (Libaijia) Lin
Sebastian Sandoval
- [email protected]