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Agent (Categorical)
Multi-Step Agent (Categorical)
Agent (Categorical)Claude & GPT-4
5-step agent pipeline: Extract Facts > Web Search > Clinical Assessment > Market Dynamics > Final Prediction. Uses specialized prompts at each step and aggregates findings for a more informed prediction.
Overview
Strategy Type
Agent (Categorical)
Number of Steps
5 steps
Models Used
Claude & GPT-4
Output Format
Categorical (very_negative to very_positive)
Pipeline Visualization
1
Step 1: Extract Facts
LLM prompt execution
2
Step 2: Web Search
Automated web search (no LLM call)
3
Step 3: Clinical Assessment
LLM prompt execution
4
Step 4: Market Dynamics
LLM prompt execution
5
Step 5: Final Prediction
LLM prompt execution
Prompt Details
1
Step 1: Extract Facts
You are a biotech analyst extracting key facts from press releases. Be precise and factual.
Extract the key clinical/regulatory facts from this press release.
PRESS RELEASE:
{pr_text}
Return a JSON object with:
{{
"primary_endpoint_met": true/false/null,
"p_value": "<p-value if mentioned, else null>",
"hazard_ratio": "<HR if mentioned, else null>",
"confidence_interval": "<CI if mentioned, else null>",
"effect_size": "<key efficacy metric>",
"safety_signals": ["list of safety concerns mentioned"],
"regulatory_status": "<FDA status or null>",
"trial_phase": "<Phase 1/2/3 or null>",
"patient_population": "<description of patients>",
"key_quotes": ["1-2 important quotes"]
}}
Expected Output Format
This prompt expects a JSON response. See the user prompt template for the exact structure.
2
Step 2: Web Search
Automated(Automated web search step - not an LLM prompt)
Search for {ticker} stock biotech news and {drug} {company} analyst expectations
3
Step 3: Clinical Assessment
You are a clinical trial expert assessing the medical significance of trial results.
Based on these extracted facts, assess the clinical significance.
EXTRACTED FACTS:
{step1_result}
CATALYST INFO:
- Drug: {drug}
- Phase: {phase}
- Indication: {indication}
Answer these questions in JSON:
{{
"clinical_significance": "breakthrough/meaningful/incremental/neutral/disappointing/failure",
"vs_standard_of_care": "<comparison>",
"unmet_need_addressed": "<assessment>",
"approvability_assessment": "<likely/uncertain/unlikely>",
"clinical_reasoning": "<2-3 sentences>"
}}
Expected Output Format
This prompt expects a JSON response. See the user prompt template for the exact structure.
4
Step 4: Market Dynamics
You are a biotech equity analyst evaluating market dynamics and investor expectations.
Evaluate the market dynamics for this catalyst.
CLINICAL ASSESSMENT:
{step3_result}
MARKET CONTEXT FROM WEB:
{step2_result}
CATALYST INFO:
- Ticker: {ticker}
- Company: {company}
- Drug: {drug}
- Phase: {phase}
- Event Type: {cat_type}
Return JSON:
{{
"priced_in_assessment": "fully_priced_in/partially_priced_in/not_priced_in/negatively_positioned",
"market_expectations": "exceeded/met/missed",
"asset_importance": "lead_asset/key_pipeline/minor_program",
"market_opportunity": "blockbuster/significant/moderate/niche",
"market_dynamics_reasoning": "<2-3 sentences>"
}}
Expected Output Format
This prompt expects a JSON response. See the user prompt template for the exact structure.
5
Step 5: Final Prediction
You are a senior biotech equity analyst making stock price predictions based on catalyst analysis.
Make your final stock price impact prediction based on all the analysis.
STEP 1 - EXTRACTED FACTS:
{step1_result}
STEP 3 - CLINICAL ASSESSMENT:
{step3_result}
STEP 4 - MARKET DYNAMICS:
{step4_result}
CATALYST INFO:
- Ticker: {ticker}
- Company: {company}
- Drug: {drug}
IMPORTANT: Be realistic. Most FDA approvals after successful Phase 3 are neutral or slightly_positive because they're priced in.
Return your final prediction:
{{
"predicted_impact": "very_positive|positive|slightly_positive|neutral|slightly_negative|negative|very_negative",
"score": <0-100>,
"reasoning": "<2-3 sentence summary>",
"key_factors": ["factor 1", "factor 2", "factor 3"]
}}
Template Variables Reference
These variables are dynamically replaced with actual values when the strategy is executed:
{cat_type}Catalyst event type (e.g., FDA approval, trial results){company}Company name{drug}Drug or therapy name{indication}Medical indication/disease being treated{phase}Clinical trial phase (1, 2, 3, etc.){pr_text}Full press release text{step1_result}Output from Step 1 prompt{step2_result}Output from Step 2 (web search){step3_result}Output from Step 3 prompt{step4_result}Output from Step 4 prompt{ticker}Company stock ticker symbol{{
"clinical_significance": "breakthrough/meaningful/incremental/neutral/disappointing/failure",
"vs_standard_of_care": "<comparison>",
"unmet_need_addressed": "<assessment>",
"approvability_assessment": "<likely/uncertain/unlikely>",
"clinical_reasoning": "<2-3 sentences>"
}Custom variable{{
"predicted_impact": "very_positive|positive|slightly_positive|neutral|slightly_negative|negative|very_negative",
"score": <0-100>,
"reasoning": "<2-3 sentence summary>",
"key_factors": ["factor 1", "factor 2", "factor 3"]
}Custom variable{{
"priced_in_assessment": "fully_priced_in/partially_priced_in/not_priced_in/negatively_positioned",
"market_expectations": "exceeded/met/missed",
"asset_importance": "lead_asset/key_pipeline/minor_program",
"market_opportunity": "blockbuster/significant/moderate/niche",
"market_dynamics_reasoning": "<2-3 sentences>"
}Custom variable{{
"primary_endpoint_met": true/false/null,
"p_value": "<p-value if mentioned, else null>",
"hazard_ratio": "<HR if mentioned, else null>",
"confidence_interval": "<CI if mentioned, else null>",
"effect_size": "<key efficacy metric>",
"safety_signals": ["list of safety concerns mentioned"],
"regulatory_status": "<FDA status or null>",
"trial_phase": "<Phase 1/2/3 or null>",
"patient_population": "<description of patients>",
"key_quotes": ["1-2 important quotes"]
}Custom variable