334 lines
13 KiB
Python
334 lines
13 KiB
Python
import os
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import chromadb
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from chromadb.config import Settings
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from openai import OpenAI
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from typing import List, Dict, Any, Optional, Tuple
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class FinancialSituationMemory:
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def __init__(self, name, config):
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# Determine embedding backend URL
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# For Ollama, use the Ollama endpoint; otherwise default to OpenAI for embeddings
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if config.get("backend_url") == "http://localhost:11434/v1":
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self.embedding_backend = "http://localhost:11434/v1"
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self.embedding = "nomic-embed-text"
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else:
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# Always use OpenAI for embeddings, regardless of LLM provider
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self.embedding_backend = "https://api.openai.com/v1"
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self.embedding = "text-embedding-3-small"
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self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# Use persistent storage in project directory
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persist_directory = os.path.join(config.get("project_dir", "."), "memory_db")
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os.makedirs(persist_directory, exist_ok=True)
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self.chroma_client = chromadb.PersistentClient(path=persist_directory)
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# Get or create collection
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try:
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self.situation_collection = self.chroma_client.get_collection(name=name)
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except:
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self.situation_collection = self.chroma_client.create_collection(name=name)
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def get_embedding(self, text):
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"""Get OpenAI embedding for a text"""
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response = self.client.embeddings.create(
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model=self.embedding, input=text
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)
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return response.data[0].embedding
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def add_situations(self, situations_and_advice):
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"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
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situations = []
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advice = []
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ids = []
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embeddings = []
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offset = self.situation_collection.count()
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for i, (situation, recommendation) in enumerate(situations_and_advice):
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situations.append(situation)
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advice.append(recommendation)
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ids.append(str(offset + i))
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embeddings.append(self.get_embedding(situation))
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self.situation_collection.add(
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documents=situations,
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metadatas=[{"recommendation": rec} for rec in advice],
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embeddings=embeddings,
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ids=ids,
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)
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def add_situations_with_metadata(
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self,
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situations_and_outcomes: List[Tuple[str, str, Dict[str, Any]]]
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):
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"""
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Add financial situations with enhanced metadata for learning system.
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Args:
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situations_and_outcomes: List of tuples (situation_text, recommendation, metadata)
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where metadata contains:
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- ticker: Stock symbol
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- analysis_date: Date of analysis (YYYY-MM-DD)
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- days_before_move: How many days before the major move (7 or 30)
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- move_pct: Percentage move that occurred
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- move_direction: "up" or "down"
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- agent_recommendation: What the agent recommended
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- was_correct: Boolean, whether recommendation matched outcome
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- structured_signals: Dict of signal features (optional)
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- unusual_volume: bool
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- analyst_sentiment: str (bullish/bearish/neutral)
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- news_sentiment: str (positive/negative/neutral)
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- short_interest: str (high/medium/low)
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- insider_activity: str (buying/selling/none)
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- etc.
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"""
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situations = []
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ids = []
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embeddings = []
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metadatas = []
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offset = self.situation_collection.count()
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for i, (situation, recommendation, metadata) in enumerate(situations_and_outcomes):
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situations.append(situation)
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ids.append(str(offset + i))
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embeddings.append(self.get_embedding(situation))
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# Merge recommendation with metadata
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full_metadata = {"recommendation": recommendation}
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full_metadata.update(metadata)
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# Ensure all metadata values are strings, numbers, or booleans for ChromaDB
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full_metadata = self._sanitize_metadata(full_metadata)
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metadatas.append(full_metadata)
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self.situation_collection.add(
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documents=situations,
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metadatas=metadatas,
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embeddings=embeddings,
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ids=ids,
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)
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def _sanitize_metadata(self, metadata: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Sanitize metadata for ChromaDB compatibility.
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ChromaDB requires metadata values to be str, int, float, or bool.
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Nested dicts are flattened with dot notation.
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"""
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sanitized = {}
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for key, value in metadata.items():
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if isinstance(value, dict):
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# Flatten nested dicts
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for nested_key, nested_value in value.items():
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flat_key = f"{key}.{nested_key}"
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if isinstance(nested_value, (str, int, float, bool, type(None))):
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sanitized[flat_key] = nested_value if nested_value is not None else "none"
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elif isinstance(value, (str, int, float, bool, type(None))):
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sanitized[key] = value if value is not None else "none"
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else:
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# Convert other types to string
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sanitized[key] = str(value)
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return sanitized
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def get_memories(self, current_situation, n_matches=1):
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"""Find matching recommendations using OpenAI embeddings"""
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query_embedding = self.get_embedding(current_situation)
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results = self.situation_collection.query(
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query_embeddings=[query_embedding],
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n_results=n_matches,
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include=["metadatas", "documents", "distances"],
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)
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matched_results = []
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for i in range(len(results["documents"][0])):
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matched_results.append(
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{
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"matched_situation": results["documents"][0][i],
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"recommendation": results["metadatas"][0][i]["recommendation"],
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"similarity_score": 1 - results["distances"][0][i],
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}
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)
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return matched_results
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def get_memories_hybrid(
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self,
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current_situation: str,
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signal_filters: Optional[Dict[str, Any]] = None,
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n_matches: int = 3,
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min_similarity: float = 0.5
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) -> List[Dict[str, Any]]:
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"""
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Hybrid search: Filter by structured signals, then rank by embedding similarity.
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Args:
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current_situation: Text description of current market situation
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signal_filters: Dict of structured signals to filter by (e.g., {"unusual_volume": True})
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Supports exact matches and can use dot notation for nested fields
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e.g., {"structured_signals.unusual_volume": True}
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n_matches: Number of results to return
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min_similarity: Minimum similarity score (0-1) to include in results
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Returns:
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List of dicts with keys:
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- matched_situation: Historical situation text
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- recommendation: What was recommended
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- similarity_score: Embedding similarity (0-1)
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- metadata: Full metadata including outcome, signals, etc.
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"""
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query_embedding = self.get_embedding(current_situation)
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# Build where clause for filtering
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where_clause = None
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if signal_filters:
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where_clause = {}
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for key, value in signal_filters.items():
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where_clause[key] = value
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# Query ChromaDB with optional filtering
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query_params = {
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"query_embeddings": [query_embedding],
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"n_results": min(n_matches * 3, 100), # Get more results for filtering
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"include": ["metadatas", "documents", "distances"],
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}
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if where_clause:
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query_params["where"] = where_clause
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results = self.situation_collection.query(**query_params)
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# Process and filter results
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matched_results = []
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for i in range(len(results["documents"][0])):
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similarity_score = 1 - results["distances"][0][i]
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# Apply similarity threshold
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if similarity_score < min_similarity:
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continue
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metadata = results["metadatas"][0][i]
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matched_results.append({
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"matched_situation": results["documents"][0][i],
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"recommendation": metadata.get("recommendation", ""),
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"similarity_score": similarity_score,
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"metadata": metadata,
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# Extract key fields for convenience
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"ticker": metadata.get("ticker", ""),
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"move_pct": metadata.get("move_pct", 0),
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"move_direction": metadata.get("move_direction", ""),
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"was_correct": metadata.get("was_correct", False),
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"days_before_move": metadata.get("days_before_move", 0),
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})
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# Return top n_matches
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return matched_results[:n_matches]
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def get_statistics(self) -> Dict[str, Any]:
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"""
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Get statistics about the memory bank.
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Returns:
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Dict with keys:
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- total_memories: Total number of stored memories
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- accuracy_rate: % of memories where was_correct=True
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- avg_move_pct: Average percentage move in stored outcomes
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- signal_distribution: Count of different signal patterns
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"""
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total_count = self.situation_collection.count()
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if total_count == 0:
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return {
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"total_memories": 0,
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"accuracy_rate": 0.0,
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"avg_move_pct": 0.0,
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"signal_distribution": {}
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}
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# Get all memories
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all_results = self.situation_collection.get(
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include=["metadatas"]
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)
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metadatas = all_results["metadatas"]
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# Calculate statistics
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correct_count = sum(1 for m in metadatas if m.get("was_correct") == True)
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accuracy_rate = (correct_count / total_count * 100) if total_count > 0 else 0
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move_pcts = [m.get("move_pct", 0) for m in metadatas if "move_pct" in m]
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avg_move_pct = sum(move_pcts) / len(move_pcts) if move_pcts else 0
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# Count signal patterns
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signal_distribution = {}
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for metadata in metadatas:
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for key, value in metadata.items():
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if key.startswith("structured_signals."):
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signal_name = key.replace("structured_signals.", "")
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if signal_name not in signal_distribution:
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signal_distribution[signal_name] = {}
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if value not in signal_distribution[signal_name]:
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signal_distribution[signal_name][value] = 0
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signal_distribution[signal_name][value] += 1
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return {
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"total_memories": total_count,
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"accuracy_rate": accuracy_rate,
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"avg_move_pct": avg_move_pct,
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"signal_distribution": signal_distribution
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}
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if __name__ == "__main__":
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# Example usage
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matcher = FinancialSituationMemory()
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# Example data
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example_data = [
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(
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"High inflation rate with rising interest rates and declining consumer spending",
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"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
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),
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(
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"Tech sector showing high volatility with increasing institutional selling pressure",
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"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
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),
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(
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"Strong dollar affecting emerging markets with increasing forex volatility",
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"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
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),
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(
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"Market showing signs of sector rotation with rising yields",
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"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
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),
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]
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# Add the example situations and recommendations
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matcher.add_situations(example_data)
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# Example query
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current_situation = """
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Market showing increased volatility in tech sector, with institutional investors
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reducing positions and rising interest rates affecting growth stock valuations
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"""
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try:
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recommendations = matcher.get_memories(current_situation, n_matches=2)
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for i, rec in enumerate(recommendations, 1):
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print(f"\nMatch {i}:")
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print(f"Similarity Score: {rec['similarity_score']:.2f}")
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print(f"Matched Situation: {rec['matched_situation']}")
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print(f"Recommendation: {rec['recommendation']}")
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except Exception as e:
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print(f"Error during recommendation: {str(e)}")
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