A prompt is the instruction or input supplied to a generative model to guide its output. In financial applications, it can turn investment posts into a personalized digest or organize market and portfolio data for an allocation recommendation. section 2.3.1 section 12.5
Listing 2.1 Conceptual snippet for Alpha Digest summarization
import openai
from typing import List, Dict, Optional
# Best practice: Initialize the client. The API key is loaded automatically
# from the OPENAI_API_KEY environment variable.
client = openai.OpenAI()
def generate_alpha_digest(posts: List[str], user_profile: Dict) -> Optional[str]:
"""
Generates a personalized summary of investment posts using the Responses API.
Includes basic input validation and error handling for robustness.
"""
# 1. Input Validation: Ensure there are posts to analyze.
if not posts:
return "No posts provided for analysis."
# Safely read the user's name, with a neutral fallback if it is missing.
user_name = user_profile.get("name", "the user")
# Format the posts for the prompt, taking up to 3 for this example.
posts_text = "\n".join([f'- "{post}"' for post in posts[:3]])
prompt_content = f"""
You are an expert financial analyst. Based on the following posts from investors
that {user_name} follows, please provide a concise 'Alpha Digest'.
Extract the key investment thesis, any mentioned price targets, and the overall sentiment.
Posts:
{posts_text}
Your Premium Digest:
"""
try:
# 2. Modern API Call: Use the Responses API.
response = client.responses.create(
model="gpt-4o-mini", # A current, cost-effective model
input=prompt_content,
max_output_tokens=200,
temperature=0.2,
)
return response.output_text.strip()
# 3. Error Handling: Catch OpenAI API errors specifically.
except openai.APIError as e:
# In a real application, you would log this error.
print(f"An OpenAI API error occurred: {e}")
return "Analysis is temporarily unavailable."
# --- Example Usage ---
sample_posts = [
"I'm still bullish on $TECHCO. Their recent acquisition in the AI space is a game-changer. PT $150.",
"Seeing a lot of institutional buying in $ENERGIZE. The move to green energy is a long-term tailwind. Accumulating.",
"Warning on $RETAILCORP. Supply chain issues are worsening, and their margins will get squeezed. I'm staying away.",
]
user = {"name": "Jane Doe"}
digest = generate_alpha_digest(sample_posts, user)
print(digest)
See an illustrative Alpha Digest prompt workflow that formats investment posts and user information for personalized summarization. section 2.3.1
Listing 12.6 Constructing the user prompt
def build_user_prompt(macro_data, quant_signals, current_weights):
"""Construct the user prompt with all available information."""
macro_text = "\n".join([f" {k}: {v}" for k, v in macro_data.items()]) #1
quant_lines = []
for etf, sig in quant_signals.items():
line = f" {etf}: 20d={sig['momentum_20d']:+.1f}%, "
line += f"60d={sig['momentum_60d']:+.1f}%, vol={sig['volatility']:.1f}%"
quant_lines.append(line)
quant_text = "\n".join(quant_lines) #2
portfolio_text = ", ".join(
[f"{etf}:{w}%" for etf, w in current_weights.items()]
) #3
return f"""MACROECONOMIC INDICATORS:
{macro_text}
QUANTITATIVE SIGNALS:
{quant_text}
CURRENT PORTFOLIO: {portfolio_text}
Recommend target allocations.
OUTPUT (JSON only):
{{
"reasoning": "One sentence key insight",
"target_weights": {{"SPY": X, "QQQ": X, "VIXY": X, "TLT": X, "WEAT": X}},
"conviction": 1-10,
"flags": ["any constraint violations or concerns"]
}}"""
See a portfolio prompt that separates macroeconomic indicators, quantitative signals, and current holdings before requesting target allocations. section 12.5
Prompt construction is one component of a larger system. A production financial product may require reliable metadata, data processing, and a RAG pipeline; an illustrative prompt snippet does not provide that complete infrastructure. section 2.3.1 section 11.0
