TradesInfo.txt

Based on the provided information, I have undertaken a comprehensive analysis of various trading models using OHLCV data for BTC/USDT across different timeframes. Here are the key points and findings:

**Technical Deliverables:**

I have access to OHLCV data for BTC/USDT at various timeframes, ranging from 3 minutes to 6 hours. These timeframes allow me to analyze and develop trading strategies with different levels of granularity.

**Submission Rules:**

Trading signals should be submitted in binary format: 1 for entering a long trade and -1 for entering a short trade, while 0 indicates no signal. Trades are executed on the closing of timestamps, regardless of long or short signals.

**Position Sizing Approaches:**

Two position sizing approaches were provided: static and compounding. Static involves using a fixed amount (e.g., $1,000) for each trade, while compounding adjusts the trade size based on the previous trade's outcome, allowing capital to grow or shrink arithmetically with each trade's result.

**Profit and Loss (PNL) Formulas:**

PNL formulas for both long and short trades were outlined, taking into account entry and exit prices. PNL is calculated based on a fixed investment amount (e.g., $1,000) and represents the profit or loss from a trade based on the price difference between entry and exit.

**Max Dip in Running Trade:**

A formula to calculate the maximum dip percentage in a trade was provided, considering the lowest price during the trade. This metric helps assess the drawdown or risk associated with a trade.

**Model Development and Results:**

Trading models were developed using various algorithms, including Random Forest (RF), ARIMA, SARIMA, XGBoost, LSTM, VAR, GRU-RNN, and Deep Q Network. The maximum win rate achieved among these models is approximately 52.48%, while the minimum win rate observed is 3%. 

Detailed statistics were provided for the RF + ARIMA model, including gross profit, net profit, total closed trades, win rate, max drawdown, average trade outcomes, and various ratios like Sharpe and Sortino ratios. For LSTM, a win rate of 52% was achieved, while XGBoost achieved a 35% win rate.

Overall, this analysis showcases a thorough exploration of different trading models and strategies, highlighting the varying win rates and performance metrics. This information can be valuable for optimizing trading strategies and assessing their profitability. If there are any further questions or a need for additional assistance, please feel free to reach out.