Building an Automated Geopolitical Risk Analysis System for Commodity Trading with LLM-based OSINT and Causal Inference
In today's highly volatile commodity markets, geopolitical risks are like unpredictable 'black swans'. This article presents an innovative approach to building an automated system that combines Large Language Model (LLM)-based OSINT (Open Source Intelligence) collection and causal inference to analyze geopolitical risks in real-time for commodity trading decision-making. This provides traders and analysts with a competitive edge, helping them quickly and accurately derive key insights from a flood of data.
1. The Challenge / Context
Global commodity markets are extremely sensitive to geopolitical events. Wars, sanctions, changes in trade policies, and even individual statements by key figures have immediate and enormous impacts on the prices of crude oil, natural gas, grains, minerals, and more. The problem is that most of these geopolitical risks exist in the form of unstructured text data (news articles, social media, government reports, etc.), and their sheer volume makes real-time response impossible with manual human analysis. Traditional quantitative models tend to rely heavily on correlations in past price data, showing limitations in understanding the fundamental causal relationships of 'why' and 'how' complex, qualitative geopolitical factors affect commodity prices.
As a result, traders are often left with 'reactive responses', only recognizing risks after crucial information has become widely known and reflected in the market, or they are exposed to the risk of losses due to decisions based on inaccurate information and human biases. We are at a critical juncture where an automated system is desperately needed to bridge this gap, proactively and causally understand geopolitical risks, and incorporate them into trading strategies.
2. Deep Dive: LLM, OSINT, and Causal Inference
The core of our proposed system lies in the synergy of three technological components. Let's delve deeper into the role each component plays.
2.1. LLM (Large Language Models)-based Information Extraction and Summarization
- Role: This is the 'front line of information', extracting and structuring key information from unstructured text data. It identifies important geopolitical actors (countries, individuals, organizations), events (invasions, sanctions, meetings), related commodities (oil, gas, grain), and the market's potential sentiment (positive/negative/neutral) towards those events from countless news articles, reports, and social media posts.
- Technical Aspects: Performs advanced Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER), Relation Extraction, Event Extraction, Sentiment Analysis, and Summarization. Latest LLMs like GPT-4o and Llama 3 can achieve excellent performance without fine-tuning for specific domains through zero-shot/few-shot learning. The key is to obtain structured data in the desired format (e.g., JSON) through prompt engineering.
2.2. OSINT (Open Source Intelligence)-based Data Collection
- Role: This is the 'fountain of information', providing the raw data needed for the system. It involves collecting information from all publicly accessible sources.
- Data Sources:
- Major news agency APIs (Reuters, Bloomberg, AP, etc.)
- Social media streams (Twitter, Reddit, specialized geopolitical forums)
- Government agency announcements and official documents
- Think tank and academic research reports
- Satellite image analysis (text descriptions) and Geographic Information System (GIS) data
- Challenges: Managing enormous data volumes, information noise, disinformation, and the complexity of real-time processing. Cross-validation from multiple sources and a reliability assessment mechanism are essential.
2.3. Causal Inference Engine
- Role: This is the 'engine of insight', going beyond simple correlations between extracted information to identify the fundamental mechanisms of 'why' specific events affect commodity prices. It statistically supports claims like "X causes Y" and enables counterfactual analysis, such as "What would have happened to Y if X had not occurred?"
- Technical Aspects:
- Causal Graph: Constructs a Directed Acyclic Graph (DAG) that represents geopolitical events, policies, economic indicators, and commodity prices as nodes, and the causal relationships between them as directed edges. This graph can be generated through expert knowledge, LLM's relation extraction capabilities, and, in some cases, statistical methods like specific conditional independence tests.
- Inference Mechanism: Utilizes causal inference techniques such as Do-calculus, Potential Outcomes Framework, and Structural Causal Models (SCM) to quantitatively estimate the causal effect of a specific intervention (e.g., sanction announcement) on other variables (e.g., oil prices).
- Why it's important: Traditional machine learning models are good at predictions based on correlations but have limitations in estimating the effects of interventions. Since controlled experiments are impossible in geopolitical situations, causal inference is an essential tool for understanding 'what causes what' from such observational data.
3. Step-by-Step Guide / Implementation
Now, let's look at the specific steps to integrate these three core technological components to build a geopolitical risk analysis system.
Step 1: Building the OSINT Data Collection and Refinement Pipeline
This is the initial stage of collecting data from various sources and refining it to be suitable for analysis. Ensuring real-time capability is crucial.
import requests
import json
from datetime import datetime, timedelta
def fetch_news_data(api_key, query, sources="reuters,bloomberg", language='en', from_date=None):
"""
Fetches real-time news data via NewsAPI.
from_date: YYYY-MM-DD format. Fetches articles published after the specified date.
"""
if from_date is None:
from_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') # Last 24 hours
url = f"https://newsapi.org/v2/everything?q={query}&sources={sources}&language={language}&from={from_date}&sortBy=publishedAt&apiKey={api_key}"
try:
response = requests.get(url, timeout=10) # Set 10-second timeout
response.raise_for_status() # Raise an exception for HTTP errors
data = response.json()
if data['status'] == 'ok':
# Deduplication and basic filtering (e.g., exclude articles with too short content)
articles = []
seen_titles = set()
for article in data['articles']:
if article['title'] not in seen_titles and article['content'] and len(article['content']) > 100:
articles.append(article)
seen_titles.add(article['title'])
return articles
else:
print(f"Error fetching data from NewsAPI: {data.get('message', 'Unknown error')}")
return []
except requests.exceptions.RequestException as e:
print(f"Network or API request error: {e}")
return []
# Example usage (API key should be managed as an environment variable in actual deployment)
# NEWS_API_KEY = "YOUR_NEWSAPI_KEY"
# geopolitical_keywords = "Ukraine war, Russia sanctions, Middle East conflict, oil supply, natural gas dispute"
# recent_articles = fetch_news_data(NEWS_API_KEY, geopolitical_keywords, sources="reuters,bloomberg,wsj")
# print(f"Fetched {len(recent_articles)} articles.")
# if recent_articles:
# print(json.dumps(recent_articles[0], indent=2, ensure_ascii=False))
# Social media data collection (conceptual)
# def fetch_social_media_data(platform_api_key, query_hashtags):
# # Use Twitter API (v2) or Reddit API etc. to collect relevant tweets/posts
# # Real-time streaming or periodic polling method
# # Return value: text, user, timestamp, likes/retweets count, etc.
# pass
Step 2: LLM-based Information Extraction and Structuring
Extract key elements of geopolitical events from collected unstructured text and transform them into structured data that can be used for causal inference. Prompt engineering is important.
from openai import OpenAI
import os
import json
# API keys are safer to load from environment variables.
# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# client = OpenAI(api_key=OPENAI_API_KEY)
def extract_geopolitical_features_with_llm(text):
"""
Extracts geopolitical information from the given text using an LLM.
"""
prompt = f"""
You are a skilled geopolitical analyst. Analyze the given news article text and extract the following information in JSON format.
Refer to the description and examples for each field to respond accurately and in detail.
Output Format:
{{
"event_summary": "A concise summary of the main geopolitical event described in the article (e.g., Russia's invasion of Ukraine, OPEC+'s decision to cut oil production)",
"involved_actors": [
{{"type": "Country", "name": "Country Name"}},
{{"type": "International Organization", "name": "Organization Name"}},
{{"type": "Company", "name": "Company Name"}},
{{"type": "Individual", "name": "Person Name"}}
],
"related_commodities": ["Names of commodities likely to be affected (e.g., Crude Oil, Natural Gas, Wheat, Gold, Nickel)"],
"direct_impact_keywords": ["Keywords describing the direct impact the event may have on commodity markets (e.g., Supply Reduction, Demand Increase, Export Restrictions, Upward Price Pressure, Production Increase)"],
"geopolitical_sentiment": "Overall sentiment regarding the article's tone and the event's potential impact (Positive, Negative, Neutral)",
"confidence_score": "Reliability of the extracted information (1-5 scale, 5 being highest)"
}}
If relevant information is not present in the text, leave the field empty (empty array for arrays) or mark it as "N/A".
Text:
{text}
"""
try:
response = client.chat.completions.create(
model="gpt-4o", # Use the latest model for higher accuracy
messages=[{"role": "user", "content": prompt}],
response_format={"type": "json_object"}, # Force JSON output
temperature=0.0 # Focus on accuracy rather than creativity
)
return json.loads(response.choices[0].message.content)
except Exception as e:
print(f"LLM extraction error: {e}")
return None
# Example usage
# sample_text = """
# According to Reuters, Russian state energy company Gazprom unexpectedly completely halted natural gas supplies via the Nord Stream 1 pipeline to Europe. While it claimed maintenance issues, the European Union views it as political retaliation by Russia. Following this news, European natural gas prices surged, and international crude oil prices also showed signs of rising.
# """
# extracted_data = extract_geopolitical_features_with_llm(sample_text)
# if extracted_data:
# print(json.dumps(extracted_data, indent=2, ensure_ascii=False))
Step 3: Building and Utilizing the Causal Inference Engine
Based on the extracted structured data, model and infer the causal pathways through which specific geopolitical events affect commodity prices. The Causal Graph is key.
import networkx as nx
import pandas as pd
# from dowhy import CausalModel # Advanced causal inference library (if more complex modeling is needed)
class GeopoliticalCausalEngine:
def __init__(self, expert_defined_graph=None):
"""
Initializes the causal graph.
expert_defined_graph: A dictionary of causal relationships defined by experts (e.g., {"A": ["B", "C"], "B": ["D"]})
"""
self.causal_graph = nx.DiGraph()
if expert_defined_graph:
for parent, children in expert_defined_graph.items():
for child in children:
self.causal_graph.add_edge(parent, child)
# Example of default geopolitical causal relationships (initial model)
self._add_default_geopolitical_edges()
def _add_default_geopolitical_edges(self):
"""
Adds key geopolitical causal relationships to the graph.
This can be continuously updated with the help of LLMs.
"""
self.causal_graph.add_edge("Military_Conflict", "Oil_Supply_Disruption")
self.causal_graph.add_edge("Oil_Supply_Disruption", "Oil_Price_Increase")
self.causal_graph.add_edge("Sanctions_on_Major_Producer", "Oil_Supply_Reduction")
self.causal_graph.add_edge("Sanctions_on_Major_Producer", "Financial_Market_Volatility")
self.causal_graph.add_edge("Gas_Supply_Cut_Europe", "European_Gas_Price_Surge")
self.causal_graph.add_edge("European_Gas_Price_Surge", "Global_LNG_Price_Increase")
self.causal_graph.add_edge("Extreme_Weather_Event_Commodity_Region", "Agricultural_Output_Decrease")
self.causal_graph.add_edge("Agricultural_Output_Decrease", "Food_Commodity_Price_Increase")
self.causal_graph.add_edge("Political_Instability_Developing_Nation", "Mine_Production_Disruption")
self.causal_graph.add_edge("Mine_Production_Disruption", "Base_Metal_Price_Increase")
self.causal_graph.add_edge("Inflation_Fear", "Gold_Demand_Increase")
self.causal_graph.add_edge("Gold_Demand_Increase", "Gold_Price_Increase")
def update_graph_from_llm_output(self, llm_extracted_data):
"""
Updates the causal graph or proposes new relationships based on information extracted by the LLM.
This part is useful when the LLM returns 'potential_causality_keywords', etc.
"""
event_summary = llm_extracted_data.get("event_summary")
direct_impact_keywords = llm_extracted_data.get("direct_impact_keywords", [])
related_commodities = llm_extracted_data.get("related_commodities", [])
if event_summary:
# Add event summary as a node
self.causal_graph.add_node(event_summary)
for impact_kw in direct_impact_keywords:
# Infer relationships through direct impact keywords suggested by the LLM
# E.g.: "Supply Reduction" -> "Commodity Price Increase"
# More sophisticated mapping logic is needed here.
if "감소" in impact_kw or "제한" in impact_kw or "중단" in impact_kw:
for commodity in related_commodities:
impact_node = f"{commodity}_Supply_Reduction"
price_node = f"{commodity}_Price_Increase"
self.causal_graph.add_edge(event_summary, impact_node)
self.causal_graph.add_edge(impact_node, price_node)
# Complex logic is needed to analyze LLM's 'direct_impact_keywords' and add nodes and edges.
# One could also directly ask the LLM to 'present causal relationships in DAG form'.
def infer_causal_path(self, initiating_event_node, target_commodity_price_node):
"""
Finds causal paths from a given initiating event node to a target commodity price node.
"""
try:
# Find all simple paths
paths = list(nx.all_simple_paths(self.causal_graph, source=initiating_event_node, target=target_commodity_price_node))
if paths:
return {"found_paths": [list(path) for path in paths]}
else:
return {"found_paths": [], "message": f"No direct causal path found from '{initiating_event_node}' to '{target_commodity_price_node}'."}
except nx.NetworkXNoPath:
return {"found_paths": [], "message": f"No path exists from '{initiating_event_node}' to '{target_commodity_price_node}'."}
except nx.NodeNotFound as e:
return {"found_paths": [], "message": f"Node not found in graph: {e}"}
# Example usage
# engine = GeopoliticalCausalEngine()
# # Update graph with LLM extracted data
# # engine.update_graph_from_llm_output(extracted_data)
# # Infer causal path
# causal_result_oil = engine.infer_causal_path("Gas_Supply_Cut_Europe", "Oil_Price_Increase")
# print("\nCausal path from Gas Supply Cut to Oil Price:")
# print(json.dumps(causal_result_oil, indent=2, ensure_ascii=False))
# causal_result_gold = engine.infer_causal_path("Inflation_Fear", "Gold_Price_Increase")
# print("\nCausal path from Inflation Fear to Gold Price:")
# print(json.dumps(causal_result_gold, indent=2, ensure_ascii=False))
Step 4: Building a Risk Assessment and Automated Alert System
Combine LLM extraction information and causal inference results to evaluate final risks, and send immediate alerts to traders if defined thresholds are exceeded. This can lead to automated orders or position adjustments through API integration with trading platforms.
def assess_risk_and_alert(llm_extracted_info, causal_engine_results, current_commodity_prices):
"""
Assesses risk and generates alerts based on LLM extracted information and causal inference results.
"""
alerts = []
event_summary = llm_extracted_info.get("event_summary", "Unknown Event")
sentiment = llm_extracted_info.get("geopolitical_sentiment", "Neutral")
related_commodities = llm_extracted_info.get("related_commodities", [])
base_risk_score = 0
if sentiment == '부정적': # Negative
base_risk_score += 5
elif sentiment == '긍정적': # Positive events can also be a risk to existing positions.
base_risk_score += 1
for commodity in related_commodities:
target_node = f"{commodity}_Price_Increase" # Assume commodity price increase node
causal_paths_info = causal_engine_results.get(target_node, {"found_paths": []}) # Get information for the commodity from causal engine results.
commodity_risk_score = base_risk_score
if causal_paths_info.get("found_paths"):
commodity_risk_score += 3 # Weight risk score if causal path is confirmed
path_description = "; ".join([" -> ".join(p) for p in causal_paths_info["found_paths"]])
else:
path_description = causal_paths_info.get("message", "No strong causal path found.")
commodity_risk_score += 1 # Consider potential impact even if no direct causal path
# Additional risk assessment logic can be added by comparing with current commodity price data
current_price = current_commodity_prices.get(commodity)
if current_price and "Price_Increase" in target_node and sentiment == '부정적': # Negative
# Example: Assign weight if price fluctuation prediction exceeds a certain threshold
# (This part requires integration of a prediction model)
pass
if commodity_risk_score >= 5: # Set threshold
alerts.append({
"severity": "High",
"commodity": commodity,
"event_summary": event_summary,
"geopolitical_sentiment": sentiment,
"causal_insight": path_description,
"risk_score": commodity_risk_score,
"recommended_action": f"Thoroughly review {commodity}-related positions and consider risk hedging"
})
elif commodity_risk_score >= 3:
alerts.append({
"severity": "Medium",
"commodity": commodity,
"event_summary": event_summary,
"geopolitical_sentiment": sentiment,
"causal_insight": path_description,
"risk_score": commodity_risk_score,
"recommended_action": f"Monitor {commodity} market trends and analyze potential impacts"
})
return alerts
# Function to integrate the entire workflow (conceptual)
# def run_geopolitical_risk_analysis():
# # 1. Collect latest OSINT data
# # articles = fetch_news_data(...)
#
# # 2. Extract information with LLM
# # all_extracted_data = [extract_geopolitical_features_with_llm(article['content']) for article in articles]
# # filtered_data = [d for d in all_extracted_data if d is not None and d.get("confidence_score", 0) >= 3]
#
# # 3. Initialize causal engine and update with LLM results
# # causal_engine = GeopoliticalCausalEngine()
# # for data in filtered_data:
# # causal_engine.update_graph_from_llm_output(data)
# # # Logic can be inserted here to further query the LLM about which Commodity_Price_Increase this event might lead to
# # # E.g.: causal_engine_results[f"{commodity}_Price_Increase"] = causal_engine.infer_causal_path(data["event_summary"], f"{commodity}_Price_Increase")
#
# # 4. Risk assessment and alerts
# # current_prices = {"Oil": 85.0, "Natural_Gas": 3.2, "Wheat": 650.0}
# # final_alerts = []
# # for data in filtered_data:
# # # For each extracted event, assess risk based on causal engine results
# # # In actual implementation, specific causal path exploration results for each event should be passed
# # mock_causal_results_for_event = {
# # "Oil_Price_Increase": causal_engine.infer_causal_path(data["event_summary"], "Oil_Price_Increase"),
# # "Natural_Gas_Price_Increase": causal_engine.infer_causal_path(data["event_summary"], "Natural_Gas_Price_Increase")
# # }
# # event_alerts = assess_risk_and_alert(data, mock_causal_results_for_event, current_prices)
# # final_alerts.extend(event_alerts)
#
# # if final_alerts:
# # print("\n--- Final Risk Alerts ---")
# # for alert in final_alerts
