AI trading agent: Reinforcement learning

Previous section: AI trading agent: Fusing LLM adapters and converting to Ollama

Project repository: Web3 AI trading agent

Reinforcement learning (RL) enables models to learn from market interactions and improve decision-making through experience. RL is particularly useful for trading because it provides clear reward signals (portfolio performance) and handles sequential decision-making.

Our RL system is using DQN—the most popular algorithm.

Deep Q-Network (DQN) for trading

DQN enables our model to learn optimal trading policies through continuous trial and error, building expertise over time. At its core, DQN uses a neural network to approximate the Q-function, denoted as Q(s,a), which represents the expected future reward when taking action a in a given state s. A separate target network ensures training stability by providing consistent learning targets, and experience replay helps the model learn efficiently from varied historical market scenarios. We’ve adapted DQN specifically for trading by customizing the state representation to include market data and current portfolio positions. The actions map directly to trading decisions—buy, sell, or hold. The reward function evaluates portfolio returns and uses risk penalties to guide the trading behavior. Each training “episode” corresponds to a clearly defined trading session, complete with precise start and end conditions.

Gymnasium environment implementation

The trading environment (off-chain/rl_trading/trading.py) implements a Gymnasium-compatible interface. State representation:

Price changes: Window of recent price percentage changes
Volume and volatility: Normalized trading volume and volatility
Position indicator: Whether currently holding ETH (1) or USDC (0)

Action space design:

Action 0: HOLD (maintain current position)
Action 1: BUY (purchase ETH with available USDC)
Action 2: SELL (sell ETH for USDC)

The reward function is based on:

Trading profit/loss: Percentage gain/loss from buy/sell actions
Portfolio performance: Overall portfolio value changes
Transaction costs: 0.3% trading fee penalty

DQN training

Train the reinforcement learning agent:

python off-chain/rl_trading/train_dqn.py \
  --timesteps 100000 \
  --log-dir logs/ \
  --models-dir models/

DQN hyperparameters (hardcoded in script):

Learning rate: 1e-4 (controls adaptation speed)
Buffer size: 10,000 (experience replay capacity)
Batch size: 64 (neural network update batch size)
Gamma: 0.99 (discount factor for future rewards)
Exploration: 20% exploration fraction with epsilon decay

RL-based dataset creation

Transform the trained DQN agent’s decision-making into structured training data for language model enhancement.

Decision extraction from trained agent

Generate trading decisions across diverse market scenarios:

python off-chain/rl_trading/create_distillation_dataset.py \
  --model off-chain/models/dqn_trading_final \
  --episodes 20 \
  --steps 100 \
  --output off-chain/rl_trading/data/distillation/dqn_dataset.jsonl

MLX integration for RL enhancement

Convert RL-generated decisions into MLX-compatible training format for the second stage of fine-tuning.

At this point, you have already gone through this process with your teacher-student distillation and then further with the LoRA fine-tuning, so all of this is familiar to you.

Transform RL decisions into conversational training format:

python off-chain/rl_trading/prepare_rl_data_for_mlx.py

LoRA configuration for RL training

Check (and feel free to edit & experiment) the RL-specific LoRA configuration:

cat off-chain/rl_trading/data/rl_data/mlx_lm_prepared/rl_lora_config.yaml

Pay special attention to taking your base model and loading it with the previous LoRA fine-tuning and resuming this new RL fine-tuning on top of the previous one here in rl_lora_config.yaml:

# Load path to resume training with the given adapter weights
resume_adapter_file: "off-chain/models/trading_model_lora/adapters.safetensors"  # Continue from previous model

# Save/load path for the trained adapter weights
adapter_path: "off-chain/models/trading_model_rl_lora"

Second-stage fine-tuning execution

Execute RL-enhanced model training with careful monitoring Perform the second fine-tuning stage to integrate RL insights with language model capabilities.

mlx_lm.lora \
  --config off-chain/rl_trading/data/rl_data/mlx_lm_prepared/rl_lora_config.yaml \
  2>&1 | tee rl_training.log

RL training validation

Test the RL-model responses:

mlx_lm.generate \
  --model Qwen/Qwen2.5-3B \
  --adapter-path off-chain/models/trading_model_rl_lora \
  --prompt "Given ETH price is 2845 with volume of 950 and volatility of 0.025, recent price change of +5% in 2 hours, and I currently hold 1.2 ETH and 3500 USDC, what trading action should I take on Uniswap?" \
  --temp 0.3 \
  --max-tokens 200

Compare with base model (without RL enhancement):

mlx_lm.generate \
  --model Qwen/Qwen2.5-3B \
  --adapter-path off-chain/models/trading_model_lora \
  --prompt "Given ETH price is 2845 with volume of 950 and volatility of 0.025, recent price change of +5% in 2 hours, and I currently hold 1.2 ETH and 3500 USDC, what trading action should I take on Uniswap?" \
  --temp 0.3 \
  --max-tokens 200

Final model integration and deployment

Integrate RL-enhanced adapters with the complete trading system for optimal performance. Fuse RL-enhanced adapters into final model:

mlx_lm.fuse \
  --model Qwen/Qwen2.5-3B \
  --adapter-path off-chain/models/trading_model_rl_lora \
  --save-path off-chain/models/final_rl_trading_model \
  --upload-repo false \
  --dtype float16

Verify fusion success:

mlx_lm.generate \
  --model off-chain/models/final_rl_trading_model \
  --prompt "Given ETH price is 2500 with volume of 800 and volatility of 0.045, recent price change of -8% in 30 minutes, and I currently hold 3.0 ETH and 2000 USDC, what trading action should I take on Uniswap?" \
  --temp 0.3

Ollama deployment for RL-enhanced model

Just like before, go through the same conversion process again. Navigate to llama.cpp for conversion:

cd llama.cpp

Convert RL-enhanced model to GGUF:

python convert_hf_to_gguf.py \
  ../off-chain/models/final_rl_trading_model \
  --outtype f16 \
  --outfile ../off-chain/models/final_rl_trading_model/ggml-model-f16.gguf

Optionally quantize the model:

./build/bin/llama-quantize \
  ../off-chain/models/final_rl_trading_model/ggml-model-f16.gguf \
  ../off-chain/models/final_rl_trading_model/ggml-model-q4_k_m.gguf \
  q4_k_m

Create enhanced Ollama configuration:

cat > Modelfile-rl << 'EOF'
FROM off-chain/models/final_rl_trading_model/ggml-model-q4_k_m.gguf

# Optimized parameters for RL-enhanced model
PARAMETER temperature 0.25
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096

# No system prompt - let the RL-enhanced fine-tuned model use its learned behavior naturally
EOF

Deploy RL-enhanced model to Ollama:

ollama create trader-qwen-rl:latest -f Modelfile-rl

Test final RL-enhanced deployment:

ollama run trader-qwen-rl:latest "Given ETH price is $2506.92 with volume of 999.43 and volatility of 0.045, recent price change of 35.7765 ticks, and I currently hold 3.746 ETH and 9507.14 USDC, what trading action should I take on Uniswap?"

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Tooling

AI trading agent: Reinforcement learning

Deep Q-Network (DQN) for trading

Gymnasium environment implementation

DQN training

RL-based dataset creation

Decision extraction from trained agent

MLX integration for RL enhancement

LoRA configuration for RL training

Second-stage fine-tuning execution

RL training validation

Final model integration and deployment

Ollama deployment for RL-enhanced model

​Deep Q-Network (DQN) for trading

​Gymnasium environment implementation

​DQN training

​RL-based dataset creation

​Decision extraction from trained agent

​MLX integration for RL enhancement

​LoRA configuration for RL training

​Second-stage fine-tuning execution

​RL training validation

​Final model integration and deployment

​Ollama deployment for RL-enhanced model

Deep Q-Network (DQN) for trading

Gymnasium environment implementation

DQN training

RL-based dataset creation

Decision extraction from trained agent

MLX integration for RL enhancement

LoRA configuration for RL training

Second-stage fine-tuning execution

RL training validation

Final model integration and deployment

Ollama deployment for RL-enhanced model