Blockchain mechanics

Real-Time Market Data Integration

• Data Sources: Flams gathers data from various decentralized exchanges and blockchain networks to ensure comprehensive market coverage.

• Data Collection Mechanism: Utilizes oracle services to fetch real-time price feeds and trading volumes.

• Data Validation: Implements cryptographic proofs to verify the accuracy and integrity of incoming data.

  def fetch_market_data(oracle_address):
      data = oracle.get_latest_data(oracle_address)
      return data

2. AI Decision-Making Process

• Decision Algorithms: Employs machine learning models to analyze market trends and predict future movements.

• Feedback Loops: Incorporates a continuous learning mechanism to refine predictions based on past performance.

• Decision Transparency: Records all AI decisions on the blockchain for public verification.

  def make_decision(data):
      prediction = model.predict(data)
      if prediction > threshold:
          return "BUY"
      else:
          return "SELL"

3. Smart Contract Operations

• Overview of Smart Contracts: Manages all AI operations, including data processing and decision execution.

• Functions Handled: Automates trading strategies, manages user interactions, and ensures secure fund transfers.

• Security Features: Incorporates multi-signature authentication and error handling to prevent unauthorized access.

4. Transparency and Immutability Features

• On-Chain Recording: All AI decisions and transactions are recorded on the Solana blockchain.

• Benefits of Blockchain: Provides an immutable ledger, ensuring that data cannot be altered or tampered with.

• Auditability: Allows third-party auditors to verify the AI's operations and decision-making process.

5. User Interaction and Feedback Mechanism

• User Input Channels: Users can provide feedback through a decentralized application (dApp) interface.

• Feedback Integration: Incorporates user insights to adjust AI parameters and improve performance.

• Incentives for Participation: Offers rewards in the form of native tokens for active contributors.

  function submitFeedback(feedback) {
      fetch('/api/feedback', {
          method: 'POST',
          body: JSON.stringify({ feedback: feedback }),
          headers: {
              'Content-Type': 'application/json'
          }
      });
  }

6. AI Adaptive Learning and Evolution

• Learning Methods: Utilizes reinforcement learning to adapt to new market conditions.

• Evolution Stages: Progresses through predefined stages, each introducing more complex decision-making capabilities.

• Market Adaptation: Dynamically adjusts strategies in response to volatility and market shifts.

  for episode in range(num_episodes):
      state = environment.reset()
      total_reward = 0
      while not done:
          action = agent.choose_action(state)
          next_state, reward, done, _ = environment.step(action)
          agent.learn(state, action, reward, next_state, done)
          state = next_state
          total_reward += reward
      print(f"Episode {episode}: Total Reward {total_reward}")