Revolutionize AI Trading Bots with AutoML-Based Multi-timeframe Bitcoin Price Prediction SN Computer Science

The consensus agent, presented in detail in [36], develops a trading strategy based on a set of decisions generated by fuzzy logic agents. In general, the trading platforms must offer real-time guidance on trading positions, such as when to open/close positions, whether to go long or short or when to step away from investments. These guidelines form specific trading strategies, defined by their verifiability, https://www.xcritical.com/ quantifiability, consistency, and objectivity [2]. Anyone who wants to deploy algorithmic trading bots using NLP (Natural Language Processing) must be ready for these risks.

FinGPT: Open-Source Financial Large Language Models

Algorithmic trading is not a one-time cure; it requires frequent adjustments as well as maintenance. This is as a result of changes in data form, or the need to trading bot meaning introduce new sources/indicators, or as a result of necessary reactions to emerging market conditions. Optimization is done to improve the Crypto Arbitrage Bot’s performance after backtesting, this could entail modifying risk management settings or transaction entry and exit points, among other aspects. Based on backtested data, optimization seeks to minimize decreases (periods of loss) and maximize returns. Because the output of actions is continuous, policy gradient-based optimization algorithms are chosen. One well-known algorithm in this context is the PPO (Proximal Policy Optimization) algorithm.

trading bot research paper

Stock Trading Bot Using Reinforcement Learning

The presented theoretical and practical approaches and solutions are often insufficient Cryptocurrency wallet for HFT decision support. They are characterized by low performance (insufficient to support HFT) and costly maintenance. Moreover, the problem with openness and integration of the technologies appears in most cases. The design and implementation of multi-agent systems in stock trading has been a focal point for numerous projects and research reports.

  • There are fewer human errors, less emotional investment, and faster, and among the advantages of algorithmic trading bots is the ability to backtest strategies.
  • For this purpose, we developed the conception and prototype of a multi-agent platform in our research.
  • There was a lower value of the evaluation function in the third period than in Consensus case, which may result from lower values of ratios such as the average rate of return per transaction and risk measures.
  • This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems.

Architecture and functionalities of A-trader

This chapter surveys the nascent experimental research on the interaction between human and algorithmic (bot) traders in experimental markets. We first discuss studies in which algorithmic traders are in the researcher’s hands. We then followed it up by discussing studies in which the researcher allows human traders to decide whether to employ algorithms for trading or to trade by themselves. We find that whether algorithm traders earn more profit than human traders crucially depends on the asset’s fundamental value process and the market environment. The potential impact of interactions with algorithms on the investor’s psychology is also discussed. Until now, articles have discussed the competition between multi-agent trading systems and their performance in trading scenarios [50].

A proposal for a framework for evolutionary multi-agent trading for FOREX was introduced in [18]. In this paper, the authors focused on currency trading and included the impact of FX trading spread. They used technical indicators to provide temporary features from which a decision tree defined the training strategy.

These methods are frequently integrated into multi-agent systems to enhance trading activities in the foreign exchange market (FOREX) [1]. These systems strongly emphasize high-frequency trading (HFT), short-term position openings/closings, and sophisticated algorithms that leverage robust indicators and modern technology. The goal is to generate profits by capitalizing on minimal price fluctuations, characterized by high-frequency occurrences, where profits often arise from market liquidity imbalances or short-term pricing inefficiencies. Other factors, such as central bank interventions (e.g., by increasing / reducing foreign exchange reserves) strengthen / reduce demand for a specific currency. Fundamental analysis is based on an examination of asset markets, macroeconomic indicators, and political considerations of the country to evaluate the development of the exchange rate of a particular currency. Macroeconomic indicators are measured by Gross Domestic Product, Money Supply (M1, M5, D1, W1, etc.), unemployment, inflation, foreign exchange reserves, interest rates, and productivity.

Thus, using such technical factors as indicators, other data, and previous price records these models emphasize possible trends that are not detected using traditional research. Some of the use cases include sentiment analysis with NLP applications, price prediction, and effective trading price execution. The development of algorithmic trading has been especially notable according to this report, algorithmic trading bots account for over 70% of all trade activities in the digital currencies market. As mentioned above, agents can generate decisions that may be mutually consistent or completely contradictory. This agent receives signals from decision-making agents and evaluates their performance. Through this evaluation, the Supervisor determines the agents for building investment strategies.

It is also possible to visualize the buy and sell operations alongside the price curve. Further analysis can be conducted to check if there is overfitting/underfitting of the model. The agent generates a buy signal when the coefficient value of “a” changes from positive to negative, and it generates a sell signal when the coefficient “a” changes from negative to positive. The transition of the agent’s decision is performed using the hysteresis level, defined by the coefficient value \(\delta \). The layer of Cloud Computing Agents is the system’s core that analyzes signals contained in notifications and delivers decision recommendations to the Supervisor Agent. Selected agents (especially belonging to CCA) running on A-Trader architecture are described in the next section.

This multidimensional approach to financial decision support promises to enhance investment effectiveness and contribute to the broader field of algorithmic trading. Trading decisions often encounter risk and uncertainty complexities, significantly influencing their overall performance. Recognizing the intricacies of this challenge, computational models within information systems have become essential to support and augment trading decisions. This approach holds the promise of significantly increasing the effectiveness of investments.

trading bot research paper

In this way, the Supervisor can apply various strategies to generate open/close long/short position signals. By creating automated bots for the cryptocurrency and stock markets Solulab helps with building these trading bots. Their services help with increased, profitable, and efficient trading that allows users to link brokerage accounts. Yes, Zorro is one free script-based program trading bot that uses deep-learning algorithms to automate quantitative investing, algorithmic trading, and financial research. One of the biggest advantages of trading bots is the ability to remove bias from the trading equation.

These algorithms provide particularly high effectiveness in markets with high volatility levels. In addition to constructing a large inventory, market makers could earn profits from temporary fluctuations in stock prices by changing the orders regarding the current conditions. When we have properly defined our trading environment, we can start feeding the environment with historical market data. Yfinance library is used to obtain the end-of-day price data of Apple (AAPL), from 2021–3–1 to 2022–3–1, as training data. The calculated p-values between the returns rates generated by particular strategies are less than 0.05 in all periods.

Using two moving averages a slow-moving average that evens out fluctuations and a rapid average that responds swiftly to price changes is a popular approach. This rapid average provides a buying opportunity when it crosses beyond the slow average and a sell indication when it passes below. On its official website, Gym has provided tutorials to create environments for various purposes. In addition, since we are going to use stable-baselines3 to implement deep reinforcement learning algorithms, our environment should also follow the interface from stable-baselines3. So in this article, I will try to explain the common usage of machine learning technology for quantitative trading and elaborate a detailed process of building a trading bot using Deep Reinforcement Learning.

It is well known for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments. Through years of development, Gym’s API has become the field standard for doing this. Nowadays, quantitative trading is gradually being favoured as an emerging investment method. Instead, it utilizes quantitative models based on sound investment principles and experiences.

To illustrate one of the agents based on macro-economic analysis, there is an agent called FuzzyNeuralNetIndices. The agent computes by applying Multilayer Perceptron to the trading decisions on the S&P500 and WIG indices. A visualization agent (VA) visualizes quotations, decisions, and long/short positions in the form of charts. In general terms, A-Trader is composed of agents capable of generating independent decisions. These decisions can be characterized by model building by consistency or contradiction, e.g., the two independent agents can simultaneously generate open and closed positions. Figure 1 presents an overview of the architecture and functional concept of A-Trader.

The bot is trained on historical data of S&P 500 companies and can predict optimal trading actions to maximize returns. The Cloud of Computing Agents (CCA) consists of the Basic Agents Cloud (BAC) and Intelligent Agents Cloud (IAC). BAC consists of agents that preprocess the data and calculate the fundamental technical analysis indicators. They can perform the learning process and can change their internal state and parameters. User-defined Intelligent Agents Cloud (UAC) consists of agents created by external users.


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