JojoFin is built with three essential layers: Market Environments, Agents, and Applications. For a trading task (at the top), an agent (in the middle) interacts dynamically with a market environment (at the bottom). This layered approach enables JojoFin to effectively model and navigate market conditions, making smart trading decisions.
Tired of staring at charts all day, trying to manually time the markets? Let JojoFin do the heavy lifting. This automated AI trading bot operates 24/7, ensuring that no trading opportunity is ever missed. With JojoFin, regular investors gain the advantage of automated trading, maximizing returns while requiring minimal oversight.
JojoFin is designed to act like your round-the-clock trading assistant, making high-frequency, rule-based trades to profit from market fluctuations. While traditional investing typically involves holding assets over longer periods with fewer trades, JojoFin enables you to capitalize on short-term market changes and seize opportunities with speed.
Stock market sentiment analysis is a critical component of JojoFin's trading strategy. By leveraging web scraping methods, the bot gathers sentiment data from media sources, industry reports, social media, and investor opinions. Research shows that stock prices often correlate with public sentiment about companies.
By analyzing sentiment data, JojoFin can make more informed trading decisions, predicting how stock prices are likely to move based on market sentiment. This approach ensures that the bot stays ahead of market trends and makes decisions based on both technical and fundamental signals.
JojoFin’s trading strategies are underpinned by state-of-the-art Reinforcement Learning (RL) techniques. In this context, an RL environment is designed for portfolio optimization based on advanced mathematical formulations. This environment is highly customizable and integrates seamlessly with modern RL frameworks, allowing for efficient and adaptive trading strategies.
JojoFin comes equipped with a comprehensive library that simplifies the creation of profitable trading bots across various asset classes, including Stocks, Options, Futures, FOREX, and more. You can easily backtest your strategies against historical data to validate their profitability before deploying them in live trading. This feature empowers traders to transition effortlessly from manual strategies to fully automated trading systems with confidence.
- Structure and Clarity: Organized content to clearly distinguish between different sections (Introduction, Sentiment Analysis, Reinforcement Learning).
- Readability: Removed repetitive statements and improved flow for easier reading.
- Professional Tone: Enhanced the tone to make it sound more polished and appealing.
- Technical Depth: Expanded explanations slightly to highlight the sophistication of JojoFin’s approach without overwhelming the reader.
- Engagement: Added subtle emphasis on the benefits to the user, making the bot seem like a valuable, trustworthy tool for investors.
Feel free to suggest any further refinements or let me know if you'd like to expand on specific sections.
JojoFin with the three layers: market environments, agents, and applications. For a trading task (on the top), an agent (in the middle) interacts with a market environment (at the bottom).If you're tired staring at charts all day, and doing trades manually while exploring daily market data just relax and let the bot do all the hard work. This Trading-bot operates 24/7, ensuring no trading opportunities are missed. An AI JOJO Trading Bot offers the benefits of automated trading without needing constant attention, giving regular investors a chance for higher returns. The name of the AI trading bot is JojoFin. It is like having an automatic helper that trades for you 24/7 based on set rules, quickly making lots of small trades to profit from market changes, while traditional investing involves buying assets and holding them for a long time with less frequent trades and lower risk.
Stock market sentiment analysis is one of the web scraping methods for gathering data to make informed business decisions. Research shows that stock market price movements correlate with public sentiments regarding the companies. Thus, sentiment about the company in the media, industry reports, social media reviews, or investors’ opinions can provide great insights into how the prices of stocks change
web scraping methods for gathering Stock market data including sentiment analysis to predict stock market price movements and correlate with public sentiments regarding the companies. Thus, sentiment about the company in the media, industry reports, social media reviews, or investors’ opinions can provide great insights into how the prices of stocks change.
Reinforcement Learning (RL) techniques are considered convenient for this task :
In this experiment, we present an RL environment for the portfolio optimization based on state-of-the-art mathematical formulations. The environment aims to be easy-to-use, very customizable, and have integrations with modern RL frameworks.
Jojobot is a library that will allow you to easily create trading robots that are profitable in many different asset classes, including Stocks, Options, Futures, FOREX, and more.
Check your trading strategies against historical data to make sure they are profitable before you invest in them. JojoBot makes it easy for you to do (backtest) your trading strategies and easily convert them to algorithmic trading robots.
The Library folder has three subfolders:
- applications: trading tasks,
- agents: DRL algorithms, from ElegantRL, RLlib, or Stable Baselines 3 (SB3). Users can plug in any DRL lib and play.
- meta: market environments, we merge the stable ones from the active JOOKIE repo.
Then, we employ a train-test-trade pipeline by three files: train.py, test.py, and trade.py.
Lib
├── rl
│ ├── JOJO applications
│ ├── cryptocurrency_trading
│ ├── high_frequency_trading
│ ├── portfolio_allocation
│ └── stock_trading
│ ├── agents
│ ├── elegantrl
│ ├── rllib
│ └── stablebaseline3
│ ├── meta
│ ├── data_processors
│ ├── env_cryptocurrency_trading
│ ├── env_portfolio_allocation
│ ├── env_stock_trading
│ ├── preprocessor
│ ├── data_processor.py
│ └── finrl_meta_config.py
│ ├── config.py
│ ├── config_tickers.py
│ ├── main.py
│ ├── train.py
│ ├── test.py
│ ├── trade.py
│ └── plot.py
- Pull 1 year of trading data for (Insert your stock, options or crypto) with Yahoo Finance Downloader API
- Create a simulated trading environment using real trading data.
- Train an neural network to predict that Stock Price using reinforcement learning inside this simulation with FinRL
- Once trained, backtest the predictions on the past 30 days data to compute potential returns with FinRL
- If the expectd returns are above a certain threshold, buy, else hold. If they're below a certain threshold, sell. (using Alpaca API)
In order to have this to run automatically once a day, we can deploy it to a hosting platform like Vercel with a seperate file that repeatedly executes it.
TRAIN_START_DATE = '2010-01-01'
TRAIN_END_DATE = '2021-10-01'
TRADE_START_DATE = '2021-10-01'
TRADE_END_DATE = '2023-03-01'df = YahooDownloader(start_date = TRAIN_START_DATE,
end_date = TRADE_END_DATE,
ticker_list = config_tickers.DOW_30_TICKER).fetch_data()fe = FeatureEngineer(
use_technical_indicator=True,
tech_indicator_list = INDICATORS,
use_vix=True,
use_turbulence=True,
user_defined_feature = False) env_kwargs = {
"hmax": 100,
"initial_amount": 1000000,
"num_stock_shares": num_stock_shares,
"buy_cost_pct": buy_cost_list,
"sell_cost_pct": sell_cost_list,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": INDICATORS,
"action_space": stock_dimension,
"reward_scaling": 1e-4
}
models = {
"a2c": trained_a2c,
"ddpg": trained_ddpg,
"ppo": trained_ppo,
"td3": trained_td3,
"sac": trained_sac
}
results = predict_with_models(models, e_trade_gym)
# Access results for each model
df_account_value_a2c = results["a2c"]["account_value"]
df_account_value_ddpg = results["ddpg"]["account_value"]
df_account_value_ppo = results["ppo"]["account_value"]
df_account_value_td3 = results["td3"]["account_value"]
df_account_value_sac = results["sac"]["account_value"]
#### Taining Agents Ensamble def predict_with_models(models, environment):
for model_name, trained_model in models.items():
df_account_value, df_actions = DRLAgent.DRL_prediction(
model=trained_model,
environment=environment
)
results[model_name] = {
"account_value": df_account_value,
"actions": df_actions
}
return resultsExamples for Stocks, Options, and Crypto in the notebooks provided below. Open them in Google Colab to jumpstart your journey!
| Notebooks | Open in Google Colab |
|---|---|
| Stocks Orders |  |
| Options Orders | |
| Crypto Orders | |
| Stock Trading | |
- 🤖 Real-time AI Chatbot: Engage with AI powered by Llama3 70b to request stock news, information, and charts through natural language conversation
- 📊 Interactive Stock Charts: Receive near-instant, context-aware responses with interactive TradingView charts that host live data
- 🔄 Adaptive Interface: Dynamically render TradingView UI components for financial interfaces tailored to your specific query
- ⚡ JojoFam-Powered Performance: Leverage JojoFam's cutting-edge inference technology for near-instantaneous responses and seamless user experience
- 🌐 Multi-Asset Market Coverage: Access comprehensive data and analysis across stocks, forex, bonds, and cryptocurrencies