feat: Completed quantum relativistic meta-learning simulation

.github/workflows/python-app.yml

@@ -0,0 +1,39 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest

.vscode/extensions.json

@@ -0,0 +1,6 @@
+{
+    "recommendations": [
+        "ms-python.python",
+        "tomoki1207.pdf"
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "pdf-preview.default.sidebar": true
+}

Dockerfile

@@ -0,0 +1,19 @@
+# app/Dockerfile
+
+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+COPY api.py .
+
+EXPOSE 8080
+
+CMD ["python", "api.py"]

agent.py

@@ -0,0 +1,102 @@
+import os
+import json
+import requests
+from typing import TypedDict, Annotated, List, Union
+from langgraph.graph import StateGraph, END
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.vectorstores import Chroma
+from langchain_openai import OpenAIEmbeddings # Or Google embeddings
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
+# Configuration
+API_URL = os.getenv("ENGINE_API_URL", "http://localhost:8080/simulate")
+
+class AgentState(TypedDict):
+    messages: Annotated[List[BaseMessage], "The messages in the conversation"]
+    metrics: Dict[str, Any]
+
+@tool
+def simulate_market(beta: float = 0.01, kappa: float = 0.1, lambda_cosmo: float = 0.1):
+    """
+    Triggers the Quantum-Relativistic simulation for BTC-USD using the specified 
+    physical constants. Returns T+1 boundaries and current system stability metrics.
+    """
+    payload = {
+        "beta": beta,
+        "kappa": kappa,
+        "lambda_cosmo": lambda_cosmo,
+        "num_states": 4,
+        "lyapunov_threshold": 4.20,
+        "lambda_meta": 0.05
+    }
+    response = requests.post(API_URL, json=payload)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": f"API call failed with status {response.status_code}"}
+
+# Initialize LLM
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash") # Or vertex ai
+
+# Setup Vector Store (ChromaDB)
+# Note: In production, this would be persistent
+vector_store = Chroma(
+    collection_name="topological_states",
+    embedding_function=OpenAIEmbeddings() # Placeholder
+)
+
+def call_model(state: AgentState):
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are a Quantitative Physicist specializing in Quantum-Relativistic Market Dynamics. "
+                   "Your task is to analyze the market using topological metrics (Norm deformation, Chaos limits, Lyapunov exponents). "
+                   "Translate these physical properties into actionable financial strategies. "
+                   "Always refer to the 'metric deformation' and 'Hilbert space stability' in your analysis."),
+        MessagesPlaceholder(variable_name="messages"),
+    ])
+    chain = prompt | llm.bind_tools([simulate_market])
+    response = chain.invoke(state)
+    return {"messages": [response]}
+
+def tool_node(state: AgentState):
+    tool_messages = []
+    last_message = state["messages"][-1]
+    for tool_call in last_message.tool_calls:
+        tool_result = simulate_market.invoke(tool_call["args"])
+        tool_messages.append(ToolMessage(
+            content=json.dumps(tool_result),
+            tool_call_id=tool_call["id"]
+        ))
+
+        # RAG: Store the state in ChromaDB
+        vector_store.add_texts(
+            texts=[f"State: {json.dumps(tool_result['current_state'])} | Prediction: {json.dumps(tool_result['t_plus_1'])}"],
+            metadatas=[{"timestamp": "now"}] # Simplify for example
+        )
+
+    return {"messages": tool_messages}
+
+def should_continue(state: AgentState):
+    last_message = state["messages"][-1]
+    if last_message.tool_calls:
+        return "tools"
+    return END
+
+# Build Graph
+workflow = StateGraph(AgentState)
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+
+workflow.set_entry_point("agent")
+workflow.add_conditional_edges("agent", should_continue)
+workflow.add_edge("tools", "agent")
+
+app = workflow.compile()
+
+if __name__ == "__main__":
+    # Test run
+    inputs = {"messages": [HumanMessage(content="Analyze the market with beta=0.02")]}
+    for output in app.stream(inputs):
+        print(output)

api.py

@@ -0,0 +1,164 @@
+import os
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Dict, Any
+
+app = FastAPI(title="Quantum-Relativistic Financial Engine API")
+
+class EngineConfig(BaseModel):
+    num_states: int = 4
+    beta: float = 0.01
+    kappa: float = 0.1
+    lyapunov_threshold: float = 4.20
+    lambda_meta: float = 0.05
+    lambda_cosmo: float = 0.1
+
+class QuantumRelativisticEngine:
+    def __init__(self, config: EngineConfig):
+        self.N = config.num_states
+        self.beta = config.beta
+        self.kappa = config.kappa
+        self.lyapunov_threshold = config.lyapunov_threshold
+        self.lambda_meta = config.lambda_meta
+        self.lambda_cosmo = config.lambda_cosmo
+
+        self.c = np.ones(self.N, dtype=complex) / np.sqrt(self.N)
+        self.theta = np.random.randn(self.N) * 0.01  
+        self.theta_0 = self.theta.copy()      
+
+        self.norm_state = 1.0                 
+        self.hilbert_reset_threshold = 5.0    
+
+    def compute_relativistic_metric(self, price_change, volatility):
+        kuhn_tucker_grad = price_change * volatility
+        numerator = 1.0 + np.abs(kuhn_tucker_grad)**2
+        denominator = 1.0 + self.beta * (self.norm_state**2)
+        g_factor = numerator / denominator
+        g_mu_nu = g_factor * np.array([-1.0, 1.0, 1.0, 1.0])
+        return g_mu_nu, g_factor
+
+    def compute_lyapunov_exponent(self, current_psi, previous_psi, dt=1):
+        delta_psi = np.linalg.norm(current_psi - previous_psi)
+        if delta_psi <= 1e-12:
+            return 0.0
+        jacobian_factor = np.abs(np.log(delta_psi / 1e-5) / dt)
+        return jacobian_factor
+
+    def compute_r_reg_tensor(self, g_mu_nu, lyapunov_exp, volatility, price_change):
+        R_scalar = lyapunov_exp / self.lyapunov_threshold if self.lyapunov_threshold > 0 else 0.0
+        T_mu_nu = np.diag([price_change, volatility, volatility, volatility])
+        Lambda = self.lambda_cosmo
+        kappa = self.kappa
+
+        R_mu_nu_approx = np.diag([lyapunov_exp, lyapunov_exp, lyapunov_exp, lyapunov_exp])
+        r_reg_tensor = R_mu_nu_approx - 0.5 * R_scalar * g_mu_nu + Lambda * g_mu_nu - kappa * T_mu_nu
+        return r_reg_tensor
+
+    def execute_meta_learning(self, current_loss, lr_meta=0.001):
+        grad_theta = 2 * (self.theta - self.theta_0) * current_loss
+        grad_theta = np.clip(grad_theta, -1.0, 1.0)
+        self.theta -= lr_meta * grad_theta
+        return np.sum(grad_theta**2)
+
+    def step_evolution(self, price_change, volatility, target_value_normalized):
+        prev_psi = self.c.copy()
+        g_mu_nu, g_factor = self.compute_relativistic_metric(price_change, volatility)
+        r_reg_tensor = self.compute_r_reg_tensor(g_mu_nu, self.lyapunov_threshold, volatility, price_change)
+        regulation_factor = np.mean(np.abs(r_reg_tensor))
+
+        phase_arg = np.clip(self.theta * g_factor * (1.0 - regulation_factor), -np.pi, np.pi)
+        self.c = self.c * np.exp(1j * phase_arg)
+
+        norm_c = np.linalg.norm(self.c)
+        if norm_c > 0 and not np.isnan(norm_c):
+            self.c /= norm_c
+        else:
+            self.c = np.ones(self.N, dtype=complex) / np.sqrt(self.N)
+
+        if np.linalg.norm(self.c) > self.hilbert_reset_threshold:
+            self.c = np.ones(self.N, dtype=complex) / np.sqrt(self.N)
+            self.norm_state = 1.0
+
+        lyapunov_exp = self.compute_lyapunov_exponent(self.c, prev_psi)
+        if np.isnan(lyapunov_exp) or np.isinf(lyapunov_exp):
+            lyapunov_exp = 0.0
+
+        if lyapunov_exp > self.lyapunov_threshold:
+            self.norm_state += ((lyapunov_exp - self.lyapunov_threshold) * 0.1)
+        else:
+            self.norm_state = max(1.0, self.norm_state - 0.4 * (self.norm_state - 1.0))
+
+        current_prediction = np.abs(self.c[0]) * target_value_normalized
+        loss = 0.5 * (current_prediction - target_value_normalized)**2
+        l_meta = self.execute_meta_learning(loss)
+
+        return {
+            "lyapunov": float(lyapunov_exp),
+            "norm": float(self.norm_state),
+            "loss": float(loss),
+            "prediction_norm": float(current_prediction)
+        }
+
+@app.post("/simulate")
+async def simulate(config: EngineConfig):
+    try:
+        ticker = "BTC-USD"
+        raw_data = yf.download(ticker, period="6mo", interval="1d", auto_adjust=True)
+        if raw_data.empty:
+            raise HTTPException(status_code=404, detail="No data found for BTC-USD")
+
+        if isinstance(raw_data.columns, pd.MultiIndex):
+            raw_data.columns = raw_data.columns.get_level_values(0)
+
+        close_prices = raw_data['Close'].values.astype(float).flatten()
+        df = pd.DataFrame(index=raw_data.index)
+        df['Price'] = close_prices
+        df['Returns'] = df['Price'].pct_change().fillna(0)
+        df['Volatility'] = df['Returns'].rolling(window=14).std().fillna(df['Returns'].std())
+        max_historical_price = np.max(close_prices)
+        df['Price_Normalized'] = df['Price'] / max_historical_price
+
+        engine = QuantumRelativisticEngine(config)
+        history = []
+
+        for idx, (timestamp, row) in enumerate(df.iterrows()):
+            metrics = engine.step_evolution(
+                price_change=row['Returns'],
+                volatility=row['Volatility'],
+                target_value_normalized=row['Price_Normalized']
+            )
+            history.append(metrics)
+
+        # Calculate T+1 Boundaries
+        last_close = float(df['Price'].iloc[-1])
+        last_volatility = float(df['Volatility'].iloc[-1])
+        last_norm = float(history[-1]['norm'])
+
+        spatial_compression = 1.0 / last_norm
+        max_tolerable_return = (config.lyapunov_threshold / (last_volatility * 10)) * spatial_compression
+
+        upper_chaos_limit = last_close * (1.0 + max_tolerable_return)
+        lower_chaos_limit = last_close * (1.0 - max_tolerable_return)
+
+        return {
+            "t_plus_1": {
+                "upper_limit": upper_chaos_limit,
+                "lower_limit": lower_chaos_limit,
+                "max_tolerable_return": max_tolerable_return
+            },
+            "current_state": {
+                "lyapunov": history[-1]['lyapunov'],
+                "norm": last_norm,
+                "last_price": last_close
+            },
+            "history": history[-20:] # Return last 20 steps for context
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8080)