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CHANGELOG.md

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 ### Changed
 
-- README.md
+- OVERVIEW.md
 - components/tradingview/stock-chart.tsx
 - package.json
 - pnpm-lock.yaml

MD/README.md

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+<div align="center">
+<h2>
+
+[Trading Experiments](READMExperiment.md) |
+[SnapShot](READMECodeSnapShot.md) | 
+[PaperTrading](READMExpAlpacaPaperTrading.md) | 
+[FAQ](READMEfaq.md)
+
+</h2>
+</div>
+
+# ChatGPT_Trading_Bot
+<h2 align="center">
+ <br>
+ <img src="public/groqlabs-logo-black2.png" alt="AI StockBot" width="500">
+ <br>
+ </h2>
+ 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), making sequential decisions.
+<br>
+
+## Experiment Overview
+1. Pull 1 year of trading data for (Insert your stock, options or crypto) with Yahoo Finance Downloader API
+2. Create a simulated trading environment using real trading data.
+3. Train an neural network to predict that Stock Price using reinforcement learning inside this simulation with FinRL
+4. Once trained, backtest the predictions on the past 30 days data to compute potential returns with FinRL
+5. 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. 
+
+## Dependencies 
+
+- [Python 3  ](https://www.python.org/downloads/)
+- [Alpaca SDK](https://alpaca.markets/)
+- [Vercel](https://vercel.com)
+
+#### from config file, TRAIN , TEST and TRADE days
+```python
+TRAIN_START_DATE = '2010-01-01'
+TRAIN_END_DATE = '2021-10-01'
+TRADE_START_DATE = '2021-10-01'
+TRADE_END_DATE = '2023-03-01'
+```
+#### Yahoo donloader for data frames collection from Start Train to End Tradedate
+```python
+df = YahooDownloader(start_date = TRAIN_START_DATE,
+                      end_date = TRADE_END_DATE,
+                      ticker_list = config_tickers.DOW_30_TICKER).fetch_data()
+```     
+#### Features Included DOW_30_TICKER - Technical, VIX and Turbelance INDICATORS, 
+```python
+fe = FeatureEngineer(
+                      use_technical_indicator=True,
+                      tech_indicator_list = INDICATORS,
+                      use_vix=True,
+                      use_turbulence=True,
+                      user_defined_feature = False)
+```  
+#### Envionment Aeguments
+```python
+ 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
+  }
+  ```
+#### Taining Agents Ensamble
+```python
+
+  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
+```
+#### predict_with_models Ensamble
+```python
+  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 results
+```
+## Tutorial
+
+## Google Colab Notebooks
+
+Examples 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](stocks-trading-basic.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alpacahq/alpaca-py/blob/master/examples/stocks-trading-basic.ipynb)  |
+| [Options Orders](options-trading-basic.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alpacahq/alpaca-py/blob/master/examples/options-trading-basic.ipynb) |
+| [Crypto Orders](crypto-trading-basic.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alpacahq/alpaca-py/blob/master/examples/crypto-trading-basic.ipynb)  |
+| [Stock Trading](api/tradingBot.ipynb)         |                                                 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](api/tradingBot.ipynb)                                                 |
+
+## Features
+
+- 🤖 **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