Mitigating Trading Biases with Sequential Transformers

June 9, 2025

Mitigating Trading Biases with Sequential Transformers

In September 2024, I wrote a paper on Sequential Transformers for Agentic Trading, which explored how sequential transformers, specifically Long Short-Term Memory (LSTM) networks, can be used to build trading agents that mitigate cognitive biases like overconfidence, loss aversion, and anchoring. These biases often lead to irrational trading decisions, resulting in significant financial losses. By integrating data-driven techniques and anti-bias traits, the framework enhances trading performance through disciplined, adaptive decision-making. This blog post dives into the core concepts, practical applications, and includes key code examples from the paper to illustrate how these ideas are implemented.

Understanding Cognitive Biases in Trading

Cognitive biases, such as overconfidence (overestimating market prediction ability), loss aversion (holding losing trades too long), and anchoring (fixating on initial prices), distort traders’ decisions. My paper identified 15 such biases, each quantifiable through metrics like win/loss ratios, holding periods, and Sharpe ratios. For instance, loss aversion is evident when traders hold losing trades longer than winning ones, detectable in trading logs via holding period analysis.

The goal is to design a trading agent that recognizes and counteracts these biases. Sequential transformers, with their ability to process time-series data, provide a robust solution by automating decisions and incorporating anti-bias traits to enforce rational behavior.

The Role of Sequential Transformers

Sequential transformers, particularly LSTMs, are ideal for trading due to their ability to handle sequential data like price movements, trading volumes, and sentiment scores. My paper proposed an LSTM-based trading agent that predicts actions (Buy, Hold, Sell) and adjusts them using anti-bias traits—numeric parameters that counteract specific biases. Examples include:

  • Risk Awareness (anti-overconfidence): Limits trades to high-confidence signals.
  • Loss Tolerance (anti-loss aversion): Enforces strict stop-loss thresholds (e.g., 2% loss).
  • Adaptive Decision-Making (anti-anchoring): Adjusts price targets based on new data.

These traits are stored as scores (0 to 1) and updated dynamically based on performance metrics like the Sharpe ratio, ensuring adaptability to market conditions.

Code Examples from the Paper

To bring this framework to life, my paper included a Python implementation using TensorFlow/Keras to build and train the trading agent. Below are key code snippets that demonstrate how the agent is structured, how it makes decisions, and how anti-bias traits are integrated.

1. AgenticTrader Class and Model Initialization

The AgenticTrader class initializes the LSTM model and stores anti-bias traits. The model consists of two LSTM layers with dropout for regularization, followed by dense layers to output Buy, Hold, or Sell decisions.

import numpy as np
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

class AgenticTrader:
    def __init__(self, anti_bias_traits):
        self.anti_bias_traits = anti_bias_traits
        self.model = self._build_model()
        self.scaler = StandardScaler()

    def _build_model(self):
        model = Sequential([
            LSTM(64, input_shape=(None, 5), return_sequences=True),
            Dropout(0.2),
            LSTM(32),
            Dropout(0.2),
            Dense(16, activation='relu'),
            Dense(3, activation='softmax')  # Buy, Hold, Sell
        ])
        model.compile(optimizer='adam', 
                      loss='categorical_crossentropy', 
                      metrics=['accuracy'])
        return model

This code sets up a model with 5 input features (e.g., price, volume, sentiment) and outputs probabilities for three actions. The anti-bias traits are passed as a dictionary during initialization.

2. Data Preprocessing and Decision-Making

The agent preprocesses input data and applies anti-bias traits to modify decisions, ensuring biases like overconfidence or loss aversion are mitigated.

def preprocess_data(self, data):
    scaled_data = self.scaler.fit_transform(data)
    return scaled_data

def make_decision(self, current_state):
    prediction = self.model.predict(np.array([current_state]))
    action = np.argmax(prediction)

    # Apply anti-bias traits to modify the decision
    if action == 0:  # Buy
        if np.random.random() > self.anti_bias_traits['overconfidence']['risk_awareness']:
            action = 1  # Hold
    elif action == 2:  # Sell
        if np.random.random() > self.anti_bias_traits['loss_aversion']['loss_tolerance']:
            action = 1  # Hold
    return action

Here, preprocess_data standardizes input features, and make_decision uses the model’s prediction while applying anti-bias checks. For example, a high risk_awareness score may override a Buy decision to Hold, countering overconfidence.

3. Training and Trait Updates

The agent is trained on historical data and updates its anti-bias traits based on performance metrics like the Sharpe ratio.

def train(self, X_train, y_train, epochs=100, batch_size=32):
    self.model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2)

def update_traits(self, performance_metrics):
    # Example: Adjust risk awareness based on Sharpe ratio
    if performance_metrics['sharpe_ratio'] < 1:
        self.anti_bias_traits['overconfidence']['risk_awareness'] *= 1.1  # Increase risk awareness
    else:
        self.anti_bias_traits['overconfidence']['risk_awareness'] *= 0.9  # Decrease risk awareness
    # Cap the values between 0 and 1
    self.anti_bias_traits['overconfidence']['risk_awareness'] = max(0, min(1, self.anti_bias_traits['overconfidence']['risk_awareness']))

The train method fits the model to training data, while update_traits adjusts anti-bias scores dynamically, ensuring the agent becomes more cautious if performance declines.

4. Example Usage

The paper demonstrated how to instantiate and use the agent with sample data and anti-bias traits.

# Initialize anti-bias traits
anti_bias_traits = {
    "overconfidence": {"risk_awareness": 0.8, "trade_frequency": 0.7},
    "loss_aversion": {"loss_tolerance": 0.6, "stop_loss_threshold": 0.02},
    "anchoring": {"price_reassessment_rate": 0.8},
    "herding": {"herding_resistance": 0.9}
}

# Instantiate the AgenticTrader
agent = AgenticTrader(anti_bias_traits)

# Generate dummy training data
X_train = np.random.rand(1000, 10, 5)  # 1000 samples, 10 time steps, 5 features
y_train = np.random.randint(0, 3, (1000, 3))  # One-hot encoded labels

# Train the agent
agent.train(X_train, y_train)

# Generate a random current state for testing
current_state = np.random.rand(1, 10, 5)
decision = agent.make_decision(current_state)
print(f"Agent's decision: {['Buy', 'Hold', 'Sell'][decision]}")

# Update traits based on performance
performance_metrics = {'sharpe_ratio': 0.8}
agent.update_traits(performance_metrics)

This snippet shows the full workflow: initializing traits, training the model, making a decision, and updating traits based on performance.

Practical Applications

The trading agent operates by analyzing market data (e.g., prices, volumes, sentiment) and making decisions adjusted by anti-bias traits. For example, if the model predicts a Buy but market volatility is high, a strong risk_awareness score might shift the decision to Hold, preventing overconfident trades. Similarly, a strict stop_loss_threshold ensures losing trades are exited promptly, countering loss aversion.

The feedback loop is critical: performance metrics like win/loss ratios or portfolio volatility are used to fine-tune traits. If the Sharpe ratio drops, the agent increases risk_awareness, making it more conservative. This adaptability is key in dynamic markets.

Why It Matters

Traditional trading algorithms focus on technical indicators but often ignore behavioral biases. My framework bridges behavioral finance and machine learning, explicitly addressing biases through quantifiable traits. Research, like a 2024 arXiv paper on “Quantformer,” shows transformers achieving a 17.35% annual return by reducing reliance on biased decisions. Blogs, such as DAMCO’s 2025 post, emphasize AI’s impartiality in stock market prediction, aligning with this approach.

Challenges include quantifying complex biases (e.g., self-attribution) and ensuring real-world performance. My paper addresses this through rigorous backtesting and feedback mechanisms, but further advancements, like integrating reinforcement learning, could enhance results.

Conclusion

Sequential transformers offer a powerful tool for mitigating cognitive biases in trading. By combining LSTM models with anti-bias traits, the agent achieves disciplined, data-driven decisions that adapt to market conditions.