Published April 14, 2022, 8:35 a.m. by finsteininvest
A new potential use case of deep learning is the use of it to develop a
Cryptocurrency Trader Sentiment Detector. I am currently developing a
Sentiment Analyzer on News Headlines, Reddit posts, and Twitter posts by
utilizing Recursive Neural Tensor Networks (RNTN) to provide insight
into the overall trader sentiment. Trader Sentiment is a key factor in
being able to determine cryptocurrency price movements. This article
will further discuss the benefits of Trader Sentiment Analysis for
Cryptocurrencies and the advantages RNTN’s offer for Sentiment Analysis.
The long-term vision of this project is to be able to develop an
Artificial Intelligence (AI) Cryptocurrency Trading Bot that can not
only consider trader sentiment to make trading decisions but also take
advantage of other opportunities such as arbitrage which is the purchase
and sale of an asset to profit from a difference in the price.
-> Keine Angabe, wie gut oder schlecht das Netz funktioniert.
The purpose of this series of articles is to
experiment with state-of-the-art deep reinforcement learning
technologies to see if we can create profitable Bitcoin trading bots. It seems to be the status quo to quickly shut down any attempts to
create reinforcement learning algorithms, as it is “the wrong way to go
about building a trading algorithm”. However, recent advances in the
field have shown that RL agents are often capable of learning much more
than supervised learning agents within the same problem domain. For this
reason, I am writing these articles to see just how profitable we can
make these trading agents, or if the status quo exists for a reason.
While our trading agent isn’t quite as profitable as we’d hoped, it is
definitely getting somewhere. Next time, we will improve on these
algorithms through advanced feature engineering and Bayesian
optimization to make sure our agents can consistently beat the market.
Stay tuned for my next article, and long live Bitcoin!
In the last article, we used deep reinforcement learning to create Bitcoin trading bots that don’t lose money. Although the agents were profitable, the results weren’t all that impressive, so this time we’re going to step it up a notch and massively improve our model’s profitability.
In this article, we’ve optimized our reinforcement learning agents to make
even better decisions while trading Bitcoin, and therefore, make a ton
more money! It took quite a bit of work, but we’ve managed to accomplish
it by doing the following:
- Upgrade the existing model to use a recurrent, LSTM policy network with stationary data
- Engineer 40+ new features for the agent to learn from using domain-specific technical and statistical analysis
- Improve the agent’s reward system to account for risk, instead of simply profit
- Fine tuned the model’s hyper-parameters using Bayesian optimization
- Benchmarked against common trading strategies to ensure the bots are always beating the market
Aber: Er hatte einen Fehler: Der Code konnte 12 Stunden in die Zukunft schauen...
Most AI and Deep Learning sources have a tendency to only present final
research results, which can be frustrating when trying to comprehend and
reproduce the provided solutions. Instead, I want to make this series
as educational as possible and thus will be sharing my train of thought
and all the experiments that go into the final solutions.
In conclusion, our models are able to identify the general trend of
price changes, but outliers cannot be predicted. The Mean Average
Percentage Error(MAPE) is always lower than 5, equating to an accuracy
interpretation of >95%. For the first experiments, the results are
already very promising. Nevertheless, in order to improve our model’s
shortcomings and master the outlier forecast, we have a couple of
Habe aber kein Folgeartikel gefunden
In this guide we'll discuss the application of using deep reinforcement learning for trading with TensorFlow 2.0.
Although this surely won't be the best AI trading agent of all time
(and, of course, is not recommended to trade with), it does provide a
good starting point to build off of.
Ist deep learning, aber nicht Crypto spezifisch.
This post brings together cryptos and deep learning in a desperate attempt for Reddit popularity.
I thought this was a completely unique concept to combine deep learning
and cryptos (blog-wise at least), but in researching this post (i.e.
looking for code to copy+paste), I came across something quite similar.
That post only touched on Bitcoin (the most famous crypto of them all),
but I’ll also discuss Ethereum (commonly known as ether, eth or
lambo-money). And since Ether is clearly superior to Bitcoin (have you not heard of Metropolis?), this post will definitely be better than that other one.
We’ve collected some crypto data and fed it into a supercool deeply
intelligent machine learning LSTM model. Unfortunately, its predictions
were not that different from just spitting out the previous value. How
can we make the model learn more sophisticated behaviours?
Also auch nicht erfolgreich :-(
In this study, we will use a variety of different strategies to compare
portfolio performance. Each week, we will publish an update on the
performance we saw from the previous week. The updates will include
comparisons between the different strategies, highlight different
decisions that were made, and draw conclusions about how individual
trades have been impacting the performance of the portfolio.
"Click Bait" für die Shrimply Plattform
We just launched AlgoHive, an open-source project to crowdsource the prediction of cryptocurrency prices and automate crypto trading.
We are now sharing our vision towards where our project is headed. In
short we’d like to make our group learnings and breakthroughs more
accessible, transparent and easier to contribute.
To that end I have laid out a plan that accomplishes the above while
providing a lot more structure to our efforts. AlgoHive is a free
open-source community and will always be as far as I am concerned.
Beschreibt "nur" Algohive...
TL;DR: I’ve created a formula that predicts whether you should buy or sell Bitcoin based on daily exchange price data and Google Trends keyword sentiment. The model produced a 29% return over 90 days for a $28,839 profit.
I have been testing formula of what I believe to be
a relatively consistent indicator of BTC price performance.
Specifically I was able to model a 29% profit over a 90-day period using
$100,000 as the initial investment. Note that this does not take into
account exchange trading fees which I hope solutions such as decentralized exchanges will one day eliminate.
Freqtrade is a free and open source crypto trading bot written in
Python. It is designed to support all major exchanges and be controlled
via Telegram. It contains backtesting, plotting and money management
tools as well as strategy optimization by machine learning.
We set ourselves a hard goal. In cryptocurrency trading, 95% of crypto traders lose money. Imagine you are an elementary math teacher. You have a class of 30 kids. On a Monday morning, you order your class to tell you how much they get if they multiply 32 with 78.
Only one kid gives you the right answer. No matter what year, no matter
how old they were, only one. This is pretty much the case of crypto
traders. But the kids can use a calculator any time, can they? What
about the traders? Can they also have a calculator?
From March to July 2020, we achieved a profit of +15.32%. But then there
was a big leap. By August 9, we were already at +46.1%. This result was
already satisfactory. From March to October 2020, we have achieved a +65.84% of profits.
From now on, you can put to work the same Bot that we have benefited over
60% in the last 8 months. This will require a Binance Futures account
and a subscription that costs € 59 per month. The Binance account
requires API integration. If this is a bit complicated or you don’t have
an account, check out our website for more help:
In the end, we can say that it is indeed possible to make a consistent
profit in cryptocurrency markets. And this is no longer exclusive, but
available to retailers. Look at past results and calculate how you would
have performed on your own account. We have uploaded all the trades so
far to the website. Their start and end dates are available. The pair
traded, the result brought, etc. These results can be tracked by anyone
on our website. Register and select the Bulls & Bears AI Bot!
Beschreiben aber nicht, wie es geschafft wurde.
In this Python machine learning tutorial, we have tried to understand
how machine learning has transformed the world of trading. Then we
create a simple Python machine learning algorithm to predict the next
day’s closing price for a stock.
But the question of implementing a successful strategy is still unanswered.