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quant trading for bitcoins

March 21, By Grace Quigley-Kupfer. In this research, the authors create an algorithmic trading strategy that attempts to predict the price of Bitcoin in a variety of minute intervals. Three models were used: a simple logistic regression model, a logistic regression model after Principal Component Analysis, and lastly a model using a neural network with one hidden layer. The researchers chose to study Bitcoin specifically in this experiment as this cryptocurrency has the highest volume of data available.

The data used was composed of , one minute intervals beginning January 1 of to October 11 of Once the data was cleaned, the researchers ended up with , data points. The baselines used to compare the performance of the three models were a buy and hold position on Bitcoin and a basic one feature classification model created by taking the sign of change in price from 10 minutes prior.

The first attempt to create the Bitcoin strategy was in a purely price predicting manner so the researchers used the features and current price to predict future price using linear regression. The results of this first attempt were not successful as they did not even beat either baseline. Instead, the researchers moved on to create three classification models. The first of these models was a weighted logistic regression model based on the sign of the price change.

In the second variation Principal Component Analysis, or PCA , was used to find the correlations between the features and remove the noise from the data. The last variation of the strategy used a neural network with a single hidden layer and a rectifier. The results of strategy variations were based on three main metrics: weighted average, gains, and area under the curve AUC which measures the true positive versus the false positive rate. All three models had gains that significantly outperformed the average increase per minute on bitcoin suggesting that the use of these strategies in a live trading environment could potentially be more successful than just buying and holding Bitcoin.

The first model that used weighted linear regression worked well to achieve gains and the PCA model worked well to remove some of the noise in the data. The model that used a neural network consistently outperformed the weighted average metric and the AUC metric. Risk Warning: The FXCM Group does not guarantee accuracy and will not accept liability for any loss or damage which arise directly or indirectly from use of or reliance on information contained within the webinars.

The FXCM Group may provide general commentary which is not intended as investment advice and must not be construed as such. Please ensure that you fully understand the risks involved.

To read in more depth about the Bitcoin algorithm, review the research paper by Justin Xu and Dhruv Medarametla, click here.

quant trading for bitcoins

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Sign In. Don't have an account? Join QuantConnect Today. Learn more. I was wondering if there is already a way to trade on bitcoin exchanges someone has put together. That would be sweet.

quant trading for bitcoins

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Bitcoin Trading Bot (Tutorial)

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In the event you use the Software under the license set forth under Section 1 athis Agreement will quant trading for bitcoins in effect for the duration of the evaluation or development period. Evaluation Use and Development Use License. Exchange Valet also has solid communication tools. The process is super simple, and should only take you a few minutes. Podcast: We chat with Major League Hacking about all-nighters, cup stacking, and therapy dogs. If you are looking for a platform that will give you some advanced order types, and a few basic algos, Live Trader might be overkill. When to Trade Algorithmic trading can help traders figure out the right time to make a trade based on many variables like volume, price, momentum. Bitcoin algorithmic trading functionality can be used to help traders know when to trade and how to trade.

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