Update README.md

README.md

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----
-license: mit
-language: en
-library_name: stable-baselines3
-tags:
-- reinforcement-learning
-- finance
-- gold-trading
-- xauusd
-- ppo
-metrics:
-- sharpe_ratio
-- win_rate
-pipeline_tag: reinforcement-learning
----
-
-# PPO Model for XAUUSD Gold Trading
-
-This repository contains a Reinforcement Learning model trained using Proximal Policy Optimization (PPO) for trading XAUUSD (Gold vs US Dollar) on 15-minute timeframes.
-
-## Model Details
-
-- **Model Type**: PPO (Proximal Policy Optimization)
-- **Framework**: Stable-Baselines3
-- **Environment**: Custom Gym environment for XAUUSD trading
-- **Training Data**: Historical XAUUSD data from 2004 to 2025 (resampled to 15-min bars)
-- **Total Timesteps**: 1,000,000
-- **Position Sizing**: Base 5.0 oz, Max 7.5 oz
-- **Initial Capital**: 200 USD
-- **Transaction Cost**: 0.65 USD per oz
-
-## Performance Metrics (Test Set)
-
-- **Average Daily Profit**: 51.46 USD
-- **Win Rate**: 69.0%
-- **Max Drawdown**: 12.0%
-- **Sharpe Ratio**: 7.56
-- **Average Trades per Day**: 2.66
-
-## Features Used
-
-- Log Return
-- RSI (14-period)
-- Moving Averages (short/long)
-- Bollinger Bands
-- MACD
-- Volume indicators
-
-## Usage
-
-### Loading the Model
-
-Below are two safe ways to load the trained policy depending on what you have available.
-
-Option A — Load the full Stable-Baselines3 model (.zip)
-
-```python
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import VecNormalize
-import os
-
-# Create or reconstruct an environment similar to the one used for training
-# e.g. `env = make_your_env(...)` — replace with your env factory
-env = ...
-
-# If you saved VecNormalize separately, load and wrap your env first
-if os.path.exists("models/vecnormalize.pkl"):
-	vec = VecNormalize.load("models/vecnormalize.pkl", env)
-	vec.training = False
-	vec.norm_reward = False
-	env = vec
-
-# Load the full model (policy + optimizer state)
-model = PPO.load("models/ppo_xauusd.zip", env=env)
-```
-
-Option B — Load weights saved as SafeTensors into a fresh PPO policy
-
-```python
-from safetensors.torch import load_file
-import torch
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import VecNormalize
-import os
-
-# Create or reconstruct the same environment used for training
-env = ...
-
-# If you have VecNormalize statistics, load them and wrap the env
-if os.path.exists("models/vecnormalize.pkl"):
-	vec = VecNormalize.load("models/vecnormalize.pkl", env)
-	vec.training = False
-	vec.norm_reward = False
-	env = vec
-
-# Instantiate a PPO model with the same policy architecture
-model = PPO("MlpPolicy", env)
-
-# Load SafeTensors state dict and convert values to torch.Tensor if needed
-raw_state = load_file("models/ppo_xauusd.safetensors")
-state_dict = {k: (torch.tensor(v) if not isinstance(v, torch.Tensor) else v) for k, v in raw_state.items()}
-
-# Load weights into the policy
-model.policy.load_state_dict(state_dict)
-
-# Ensure the model has the same env wrapper
-model.set_env(env)
-```
-
-Notes:
-- Option A is preferred when `ppo_xauusd.zip` is available (it contains the entire SB3 model).
-- Option B is useful when only the policy weights were exported as SafeTensors. Ensure the policy architecture and observation/action spaces match the original training setup.
-- Always set `vec.training = False` and `vec.norm_reward = False` when running inference.
-
-
-### For Full Inference
-
-To use the model for trading, you'll need to:
-1. Set up the trading environment (`XAUUSDTradingEnv`)
-2. Load VecNormalize stats
-3. Run predictions
-
-Note: This is a simulation model. Use with caution in real trading.
-
-## Training Configuration
-
-- Learning Rate: 0.0003
-- Batch Size: 256
-- Gamma: 0.99
-- GAE Lambda: 0.95
-- Clip Range: 0.2
-- Entropy Coefficient: 0.01
-
-## Files
-
-- `ppo_xauusd.safetensors`: Model weights in SafeTensors format
-- `vecnormalize.pkl`: VecNormalize statistics for observation normalization
-
-## License
-
-MIT License
-
-## Disclaimer
-
+---
+license: mit
+language: en
+library_name: stable-baselines3
+tags:
+- reinforcement-learning
+- finance
+- gold-trading
+- xauusd
+- ppo
+metrics:
+- sharpe_ratio
+- win_rate
+pipeline_tag: reinforcement-learning
+datasets:
+- ZombitX64/xauusd-gold-price-historical-data-2004-2025
+---
+
+# PPO Model for XAUUSD Gold Trading
+
+This repository contains a Reinforcement Learning model trained using Proximal Policy Optimization (PPO) for trading XAUUSD (Gold vs US Dollar) on 15-minute timeframes.
+
+## Model Details
+
+- **Model Type**: PPO (Proximal Policy Optimization)
+- **Framework**: Stable-Baselines3
+- **Environment**: Custom Gym environment for XAUUSD trading
+- **Training Data**: Historical XAUUSD data from 2004 to 2025 (resampled to 15-min bars)
+- **Total Timesteps**: 1,000,000
+- **Position Sizing**: Base 5.0 oz, Max 7.5 oz
+- **Initial Capital**: 200 USD
+- **Transaction Cost**: 0.65 USD per oz
+
+## Performance Metrics (Test Set)
+
+- **Average Daily Profit**: 51.46 USD
+- **Win Rate**: 69.0%
+- **Max Drawdown**: 12.0%
+- **Sharpe Ratio**: 7.56
+- **Average Trades per Day**: 2.66
+
+## Features Used
+
+- Log Return
+- RSI (14-period)
+- Moving Averages (short/long)
+- Bollinger Bands
+- MACD
+- Volume indicators
+
+## Usage
+
+### Loading the Model
+
+Below are two safe ways to load the trained policy depending on what you have available.
+
+Option A — Load the full Stable-Baselines3 model (.zip)
+
+```python
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import VecNormalize
+import os
+
+# Create or reconstruct an environment similar to the one used for training
+# e.g. `env = make_your_env(...)` — replace with your env factory
+env = ...
+
+# If you saved VecNormalize separately, load and wrap your env first
+if os.path.exists("models/vecnormalize.pkl"):
+	vec = VecNormalize.load("models/vecnormalize.pkl", env)
+	vec.training = False
+	vec.norm_reward = False
+	env = vec
+
+# Load the full model (policy + optimizer state)
+model = PPO.load("models/ppo_xauusd.zip", env=env)
+```
+
+Option B — Load weights saved as SafeTensors into a fresh PPO policy
+
+```python
+from safetensors.torch import load_file
+import torch
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import VecNormalize
+import os
+
+# Create or reconstruct the same environment used for training
+env = ...
+
+# If you have VecNormalize statistics, load them and wrap the env
+if os.path.exists("models/vecnormalize.pkl"):
+	vec = VecNormalize.load("models/vecnormalize.pkl", env)
+	vec.training = False
+	vec.norm_reward = False
+	env = vec
+
+# Instantiate a PPO model with the same policy architecture
+model = PPO("MlpPolicy", env)
+
+# Load SafeTensors state dict and convert values to torch.Tensor if needed
+raw_state = load_file("models/ppo_xauusd.safetensors")
+state_dict = {k: (torch.tensor(v) if not isinstance(v, torch.Tensor) else v) for k, v in raw_state.items()}
+
+# Load weights into the policy
+model.policy.load_state_dict(state_dict)
+
+# Ensure the model has the same env wrapper
+model.set_env(env)
+```
+
+Notes:
+- Option A is preferred when `ppo_xauusd.zip` is available (it contains the entire SB3 model).
+- Option B is useful when only the policy weights were exported as SafeTensors. Ensure the policy architecture and observation/action spaces match the original training setup.
+- Always set `vec.training = False` and `vec.norm_reward = False` when running inference.
+
+
+### For Full Inference
+
+To use the model for trading, you'll need to:
+1. Set up the trading environment (`XAUUSDTradingEnv`)
+2. Load VecNormalize stats
+3. Run predictions
+
+Note: This is a simulation model. Use with caution in real trading.
+
+## Training Configuration
+
+- Learning Rate: 0.0003
+- Batch Size: 256
+- Gamma: 0.99
+- GAE Lambda: 0.95
+- Clip Range: 0.2
+- Entropy Coefficient: 0.01
+
+## Files
+
+- `ppo_xauusd.safetensors`: Model weights in SafeTensors format
+- `vecnormalize.pkl`: VecNormalize statistics for observation normalization
+
+## License
+
+MIT License
+
+## Disclaimer
+
 This model is for educational and research purposes only. Trading involves risk, and past performance does not guarantee future results. Always backtest and validate before using in live trading.