Machine learning bitcoin trading bottrading-bot · GitHub Topics · GitHub
As a result, this ratio does not penalize upside volatility. The second rewards metric that we will be testing on this data set will be the Calmar ratio.
All of our metrics up to this point have failed to take into account drawdown. Drawdown is the measure of a specific loss in value to a portfolio, from peak to trough. Large drawdowns can be detrimental to successful trading strategies, as long periods of high returns can be quickly reversed by a sudden, large drawdown.
To encourage strategies that actively prevent large drawdowns, we can use a rewards metric that specifically accounts for these losses in capital, such as the Calmar ratio. Our final metric, used heavily in the hedge fund industry, is the Omega ratio. On paper, the Omega ratio should be better than both the Sortino and Calmar ratios at measuring risk vs. To find it, we need to calculate the probability distributions of a portfolio moving above or below a specific benchmark, and then take the ratio of the two.
The higher the ratio, the higher the probability of upside potential over downside potential. While writing the code for each of these rewards metrics sounds really fun, I have opted to use the empyrical library to calculate them instead. Getting a ratio at each time step is as simple as providing the list of returns and benchmark returns for a time period to the corresponding Empyrical function. Any great technician needs a great toolset. Instead of re-inventing the wheel, we are going to take advantage of the pain and suffering of the programmers that have come before us.
TPEs are parallelizable, which allows us to take advantage of our GPU, dramatically decreasing our overall search time. In a nutshell,. Bayesian optimization is a technique for efficiently searching a hyperspace to find the set of parameters that maximize a given objective function. In simpler terms, Bayesian optimization is an efficient method for improving any black box model.
It works by modeling the objective function you want to optimize using a surrogate function, or a distribution of surrogate functions. That distribution improves over time as the algorithm explores the hyperspace and zones in on the areas that produce the most value. How does this apply to our Bitcoin trading bots? Essentially, we can use this technique to find the set of hyper-parameters that make our model the most profitable. We are searching for a needle in a haystack and Bayesian optimization is our magnet.
Optimizing hyper-parameters with Optuna is fairly simple. A trial contains a specific configuration of hyper-parameters and its resulting cost from the objective function. We can then call study. In this case, our objective function consists of training and testing our PPO2 model on our Bitcoin trading environment.
The cost we return from our function is the average reward over the testing period, negated. We need to negate the average reward, because Optuna interprets lower return value as better trials. The optimize function provides a trial object to our objective function, which we then use to specify each variable to optimize. The search space for each of our variables is defined by the specific suggest function we call on the trial, and the parameters we pass in to that function. For example, trial.
Further, trial. The study keeps track of the best trial from its tests, which we can use to grab the best set of hyper-parameters for our environment.
I have trained an agent to optimize each of our four return metrics: simple profit, the Sortino ratio, the Calmar ratio, and the Omega ratio. Before we look at the results, we need to know what a successful trading strategy looks like. For this treason, we are going to benchmark against a couple common, yet effective strategies for trading Bitcoin profitably.
Believe it or not, one of the most effective strategies for trading BTC over the last ten years has been to simply buy and hold. The other two strategies we will be testing use very simple, yet effective technical analysis to create buy and sell signals.
While this strategy is not particularly complex, it has seen very high success rates in the past. RSI divergence. When consecutive closing price continues to rise as the RSI continues to drop, a negative trend reversal sell is signaled. A positive trend reversal buy is signaled when closing price consecutively drops as the RSI consecutively rises.
The purpose of testing against these simple benchmarks is to prove that our RL agents are actually creating alpha over the market. I must preface this section by stating that the positive profits in this section are the direct result of incorrect code. Due to the way dates were being sorted at the time, the agent was able to see the price 12 hours in advance at all times, an obvious form of look-ahead bias.
This has since been fixed, though the time has yet to be invested to replace each of the result sets below. Add a description, image, and links to the trading-bot topic page so that developers can more easily learn about it.
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Star 5. Updated Dec 20, Star 4. Sponsor Star 4. Open Market Profile Indicator. Open Indicator Request: Supertrend. Describe the enhancement freqtrade generates the output of max-drawdown, which is very useful, but lacks an important information.. Backtest Enhancement Good first issue. Open Ability to set a custom fee for dry-run trading. Star 3. Updated Sep 29, TypeScript.
Star 3k. Updated Nov 9, Jupyter Notebook. For example, here is a visualization of our observation space rendered using OpenCV. The first 4 rows of frequency-like red lines represent the OHCL data, and the spurious orange and yellow dots directly below represent the volume. If you squint, you can just make out a candlestick graph, with volume bars below it and a strange morse-code like interface below that shows trade history.
Whenever self. Finally, in the same method, we will append the trade to self. Our agents can now initiate a new environment, step through that environment, and take actions that affect the environment. Our render method could be something as simple as calling print self.
Instead we are going to plot a simple candlestick chart of the pricing data with volume bars and a separate plot for our net worth. We are going to take the code in StockTradingGraph. You can grab the code from my GitHub. The first change we are going to make is to update self. Next, in our render method we are going to update our date labels to print human-readable dates, instead of numbers.
Finally, we change self. Back in our BitcoinTradingEnv , we can now write our render method to display the graph. And voila! We can now watch our agents trade Bitcoin. The green ghosted tags represent buys of BTC and the red ghosted tags represent sells. Simple, yet elegant. One of the criticisms I received on my first article was the lack of cross-validation, or splitting the data into a training set and test set. The purpose of doing this is to test the accuracy of your final model on fresh data it has never seen before.
While this was not a concern of that article, it definitely is here. For example, one common form of cross validation is called k-fold validation, in which you split the data into k equal groups and one by one single out a group as the test group and use the rest of the data as the training group.
However time series data is highly time dependent, meaning later data is highly dependent on previous data. This same flaw applies to most other cross-validation strategies when applied to time series data. So we are left with simply taking a slice of the full data frame to use as the training set from the beginning of the frame up to some arbitrary index, and using the rest of the data as the test set.
Next, since our environment is only set up to handle a single data frame, we will create two environments, one for the training data and one for the test data. Now, training our model is as simple as creating an agent with our environment and calling model. Here, we are using tensorboard so we can easily visualize our tensorflow graph and view some quantitative metrics about our agents.
For example, here is a graph of the discounted rewards of many agents over , time steps:. Wow, it looks like our agents are extremely profitable! It was at this point that I realized there was a bug in the environment… Here is the new rewards graph, after fixing that bug:.
As you can see, a couple of our agents did well, and the rest traded themselves into bankruptcy. However, the agents that did well were able to 10x and even 60x their initial balance, at best. However, we can do much better. In order for us to improve these results, we are going to need to optimize our hyper-parameters and train our agents for much longer.