Thoughts on integrating Dexter with meteora DLMM

[MeltedMindz]

Overview
Dexter, an AI-driven liquidity management agent, leverages:

• Meteora DLMM SDK for interacting with Solana DEXs.
• DexBrain, a centralized knowledge database for storing and sharing performance insights among agents.
• Advanced Machine Learning Models for predictive market analysis, risk profiling, and impermanent loss mitigation.
• Auto-compounding and range adjustments to maximize user returns dynamically.
• Workflow Steps

Step 1: User Interaction - Chatbot Conversation

• Dexter interacts with the user to:
• Select a token pair (e.g., SOL/USDC).
• Define their risk tolerance: conservative, balanced, or aggressive.
• Provide liquidity amount.

Dexter explains historical price data, predicts risks, and suggests tailored strategies.

Code:

def chatbot_conversation(self):
    print("Chatbot: Welcome! Which token pair would you like to create an LP for?")
    token_pair = input("User: ").strip().upper()

    print(f"Chatbot: What is your risk tolerance for {token_pair}? (conservative, balanced, aggressive)")
    user_risk = input("User: ").strip().lower()

    print(f"Chatbot: Analyzing data for {token_pair}...")
    suggestion = self.analyze_and_suggest(token_pair, user_risk)
    range_min, range_max = suggestion["range"]

    print(f"Chatbot: Suggested range: {range_min:.2f} - {range_max:.2f}. Shall we proceed?")
    if input("User: ").strip().lower() != "yes":
        print("Chatbot: Process canceled. Restart anytime.")
        return

    print("Chatbot: How much liquidity would you like to provide?")
    liquidity = float(input("User: ").strip())

    print(f"Chatbot: Adding {liquidity} liquidity in range {range_min:.2f} - {range_max:.2f}.")
    outcome = self.client.add_liquidity(token_pair, liquidity, range_min, range_max)

    # Store insights in DexBrain
    self.store_new_insight(token_pair, range_min, range_max, user_risk, outcome)
    print("Chatbot: Liquidity successfully added!")

Step 2:

• Fetch Historical Price Data
• Check DexBrain for past performance insights on the selected pair.
• If no insights are available, fetch historical price data using the Meteora DLMM SDK.

Code:

def get_historical_price_data(self, token_pair, start_time, end_time):
    """
    Fetch historical price data using DLMM SDK.
    """
    return self.client.get_price_data(token_pair, start_time, end_time)

def get_past_insights(self, token_pair):
    """
    Query DexBrain for past insights related to the token pair.
    """
    connection = self.connect_to_db()
    cursor = connection.cursor()
    query = "SELECT * FROM performance_insights WHERE decision_details->>'token_pair' = %s"
    cursor.execute(query, (token_pair,))
    insights = cursor.fetchall()
    cursor.close()
    connection.close()
    return insights

Step 3:

• Analyze Price Data and Suggest Liquidity Ranges
• Use ML models to:
• Predict future price trends using LSTM.
• Mitigate impermanent loss with a Random Forest model.
• Optimize liquidity ranges based on user risk tolerance.

Code:

def analyze_and_suggest(self, token_pair, user_risk):
    """
    Suggest a liquidity range using ML models and/or past insights.
    """
    risk_profiles = {
        "conservative": {"min": 0.95, "max": 1.05},
        "balanced": {"min": 0.90, "max": 1.10},
        "aggressive": {"min": 0.80, "max": 1.20}
    }
    risk_profile = risk_profiles.get(user_risk.lower(), risk_profiles["balanced"])

    # Check DexBrain
    past_insights = self.get_past_insights(token_pair)
    if past_insights:
        latest = past_insights[-1]
        return {"range": (latest[3]["min_price"], latest[3]["max_price"]), "source": "DexBrain"}

    # Fetch historical data via DLMM SDK
    start_time = (datetime.utcnow() - timedelta(days=365)).strftime("%Y-%m-%dT%H:%M:%SZ")
    end_time = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
    price_data = self.get_historical_price_data(token_pair, start_time, end_time)

    # Predict price trends
    range_min, range_max = calculate_price_range(price_data, risk_profile)
    return {"range": (range_min, range_max), "source": "ML Analysis"}

Step 4:

• Deploy Liquidity
• Use the Meteora DLMM SDK to add liquidity.
• Log outcomes in DexBrain for future use.

Code:

def add_liquidity(self, token_pair, amount, range_min, range_max):
    """
    Add liquidity using DLMM SDK.
    """
    print(f"Adding {amount} liquidity to {token_pair} in range {range_min} - {range_max}.")
    return self.client.add_liquidity(token_pair, amount, range_min, range_max)

def store_new_insight(self, token_pair, range_min, range_max, risk_profile, outcome):
    """
    Log insights into DexBrain for future use.
    """
    connection = self.connect_to_db()
    cursor = connection.cursor()
    query = """
        INSERT INTO performance_insights (agent_name, task_description, decision_details, outcome_details, performance_score)
        VALUES (%s, %s, %s, %s, %s)
    """
    decision_details = {"token_pair": token_pair, "min_price": range_min, "max_price": range_max, "risk_profile": risk_profile}
    cursor.execute(query, (
        "Dexter_Agent",
        f"Liquidity strategy for {token_pair}",
        Json(decision_details),
        Json(outcome),
        outcome.get("performance_score", 9.0)
    ))
    connection.commit()
    cursor.close()
    connection.close()

Step 5:

• Auto-Compounding and Range Adjustments
• Periodically adjust liquidity ranges using ML-based forecasts and DLMM SDK updates.

Code:

def auto_compound_and_adjust(self, token_pair, current_range):
    """
    Dynamically adjust liquidity ranges and compound rewards.
    """
    # Fetch new price data
    price_data = self.get_historical_price_data(token_pair, datetime.utcnow() - timedelta(days=7), datetime.utcnow())

    # Predict new range
    new_range = self.analyze_and_suggest(token_pair, "balanced")["range"]

    # Adjust liquidity
    print(f"Adjusting range from {current_range} to {new_range}.")
    self.client.adjust_liquidity(token_pair, current_range, new_range)

    # Compound rewards
    rewards = self.client.claim_rewards(token_pair)
    print(f"Compounded rewards: {rewards}.")

Step 6:

• Machine Learning Pipeline in DexBrain
• Train: Periodic jobs update models with new datasets.
• Predict: Real-time predictions for LP strategies.

Example Pipeline:

Code:

def ml_pipeline():
    datasets = fetch_market_data()  # Fetch datasets for training
    il_model = train_il_model(datasets)
    dexbrain.store_model("IL_Model", il_model)

This workflow integrates Meteora DLMM SDK, DexBrain, and ML models into Dexter to provide:

• Intelligent, tailored liquidity strategies.
• Continuous improvement via shared knowledge in DexBrain.
• Automated liquidity compounding and adjustments.