This is the story of how I built Statera, my first AI-powered trading agent. But before diving into the journey, let’s first understand what a Statera agent is.

What is Statera?

Statera is an AI trading agent designed for spot trading BTCUSDT. Its primary goal is to generate profits exceeding the simple buy-and-hold strategy while managing risks effectively. Sounds simple, right? But in reality, it took me three years to develop a version that consistently generates profits while minimizing losses.

The Beginning: A Flawed Approach (2022)

In 2022, I had the idea of using AI to trade crypto instead of relying on manual strategies, which felt no different from gambling. I began studying algorithmic trading and quickly discovered that technical analysis was widely regarded as the key to success.

However, after extensive manual and algorithmic trading, I realized something was wrong. Despite numerous books, research papers, and tutorials claiming that technical analysis works, I couldn’t find a sustainable, repeatable strategy. It took me three years to fully grasp this fundamental issue:

Some indicators work, but most are ineffective. Correlation analysis is essential for identifying useful ones.

Backtesting is crucial, but the length of your dataset determines the reliability of your strategy. Many traders lack sufficient historical data, leading to unreliable models.

Single-timeframe trading is problematic, especially for short timeframes (1m, 5m, 15m). These are highly volatile, requiring longer historical windows or multi-timeframe analysis.

Because of these misconceptions, I wasted enormous time and computing resources training ineffective agents. By the end of 2022, after countless failed experiments, I nearly gave up. I wrongly concluded that technical analysis itself was the problem. Despite experimenting with countless indicators, window sizes, and neural network architectures, nothing worked.

The AI Renaissance: A Fresh Perspective (2024)

Fast forward to 2024—AI had advanced across industries, from image processing to autonomous vehicles. Inspired by this progress, I revisited my trading agent with fresh insights. I realized my failure wasn’t due to AI itself but because my approach was flawed.

The key issue? Overfitting.

My agent performed exceptionally well on training data but failed in real-world scenarios. Fixing this problem became my primary focus throughout 2024. While I don’t remember the exact sequence of my discoveries, here are the most critical lessons I learned:

Key Lessons from 2024

1. Input Sensitivity Matters - Agents are highly sensitive to inputs, making input diversification crucial. However, real-world data limitations exist. For example, a 1-hour timeframe dataset with 100,000 rows covers only 11.41 years—a minimum requirement for training. My initial solution of training across multiple assets failed. Instead, adding Gaussian noise to my limited dataset (50,000 rows) improved generalization.

2. Neural Network Complexity ≠ Better Performance - Initially, I assumed that increasing model complexity would yield better results. I wasted significant time tweaking hidden layers, node sizes, and hyperparameters. Instead of blindly randomizing parameters, systematic experimentation proved far more effective in finding the optimal balance between underfitting and overfitting.

3. The Market Has Seasonal Patterns - Asset prices fluctuate based on time. Incorporating time as an input feature significantly improved my agent’s performance.

4. “Reward per Step” is the Best Performance Indicator - The most effective way to measure model fitness isn’t raw profitability but rather reward per step. Crafting an optimal reward function is a complex topic I’ll cover in a future article.

5. Hyperparameter Tuning Requires Multi-Stage Training - Standard reinforcement learning (RL) tutorials often use a single set of hyperparameters. However, I found that splitting training into 2–5 phases, with gradual hyperparameter adjustments, yielded better models.

Statera’s Current Status (March 2025)

As of March 2025, Statera has been trained on BTCUSDT for approximately 4 million steps—equivalent to 456 years of historical data. The architecture consists of:

Features: Technical indicators + time-based inputs

Model: LSTM for sequential data, followed by a linear model

Training Method: PPO (Proximal Policy Optimization)

Performance Results

After testing the agent with a limited 3-month dataset, here’s what I found:

• Profitability: 20–40% gains over 3 months (Oct 2024–Jan 2025)

• Market Benchmark: BTCUSDT returned 21% during the same period

• Consistency: PPO does not produce deterministic actions but instead outputs probability distributions. Across multiple backtests, Statera consistently outperformed buy-and-hold strategies.

Given these results, I decided to release the agent for further testing under real-market conditions.

The Future: A Flagship 1-Minute Trading Model

My next goal is to develop a flagship model that trades on a 1-minute timeframe using multi-timeframe inputs. The process is ongoing, and I’ll share more insights as I refine the model. Stay tuned for future updates on my blog!

Thanks for reading!