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3 Commits
v0.4.0-alp
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v0.4.0-alp
| Author | SHA1 | Date | |
|---|---|---|---|
| abb9cd0726 | |||
| 6d126db03c | |||
| 1e7bdb509b |
@@ -419,7 +419,7 @@ class BaseAgent:
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}
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if tool_name:
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message["tool_name"] = tool_name
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message["name"] = tool_name # Use "name" not "tool_name" for consistency with summarizer
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if tool_input:
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message["tool_input"] = tool_input
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@@ -643,6 +643,11 @@ Summary:"""
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tool_msgs = extract_tool_messages(response)
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for tool_msg in tool_msgs:
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tool_name = getattr(tool_msg, 'name', None) or tool_msg.get('name') if isinstance(tool_msg, dict) else None
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tool_content = getattr(tool_msg, 'content', '') or tool_msg.get('content', '') if isinstance(tool_msg, dict) else str(tool_msg)
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# Capture tool message to conversation history
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self._capture_message("tool", tool_content, tool_name=tool_name)
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if tool_name in ['buy', 'sell']:
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action_count += 1
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@@ -52,10 +52,6 @@ class ContextInjector:
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"""
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# Inject context parameters for trade tools
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if request.name in ["buy", "sell"]:
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# Debug: Log self attributes BEFORE injection
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print(f"[ContextInjector.__call__] ENTRY: id={id(self)}, self.signature={self.signature}, self.today_date={self.today_date}, self.job_id={self.job_id}, self.session_id={self.session_id}, self.trading_day_id={self.trading_day_id}")
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print(f"[ContextInjector.__call__] Args BEFORE injection: {request.args}")
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# ALWAYS inject/override context parameters (don't trust AI-provided values)
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request.args["signature"] = self.signature
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request.args["today_date"] = self.today_date
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@@ -66,8 +62,5 @@ class ContextInjector:
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if self.trading_day_id:
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request.args["trading_day_id"] = self.trading_day_id
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# Debug logging
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print(f"[ContextInjector] Tool: {request.name}, Args after injection: {request.args}")
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# Call the actual tool handler
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return await handler(request)
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@@ -36,15 +36,17 @@ class ReasoningSummarizer:
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summary_prompt = f"""You are reviewing your own trading decisions for the day.
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Summarize your trading strategy and key decisions in 2-3 sentences.
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IMPORTANT: Explicitly state what trades you executed (e.g., "sold 2 GOOGL shares" or "bought 10 NVDA shares"). If you made no trades, state that clearly.
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Focus on:
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- What you analyzed
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- Why you made the trades you did
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- What specific trades you executed (buy/sell, symbols, quantities)
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- Why you made those trades
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- Your overall strategy for the day
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Trading session log:
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{log_text}
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Provide a concise summary:"""
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Provide a concise summary that includes the actual trades executed:"""
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response = await self.model.ainvoke([
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{"role": "user", "content": summary_prompt}
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@@ -67,21 +69,33 @@ Provide a concise summary:"""
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reasoning_log: List of message dicts
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Returns:
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Formatted text representation
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Formatted text representation with emphasis on trades
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"""
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formatted_parts = []
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trades_executed = []
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for msg in reasoning_log:
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role = msg.get("role", "")
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content = msg.get("content", "")
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tool_name = msg.get("name", "")
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if role == "assistant":
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# AI's thoughts
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formatted_parts.append(f"AI: {content[:200]}")
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elif role == "tool":
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# Tool results
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tool_name = msg.get("name", "tool")
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formatted_parts.append(f"{tool_name}: {content[:100]}")
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# Highlight trade tool calls
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if tool_name in ["buy", "sell"]:
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trades_executed.append(f"{tool_name.upper()}: {content[:150]}")
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formatted_parts.append(f"TRADE - {tool_name.upper()}: {content[:150]}")
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else:
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# Other tool results (search, price, etc.)
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formatted_parts.append(f"{tool_name}: {content[:100]}")
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# Add summary of trades at the top
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if trades_executed:
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trade_summary = f"TRADES EXECUTED ({len(trades_executed)}):\n" + "\n".join(trades_executed)
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formatted_parts.insert(0, trade_summary)
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formatted_parts.insert(1, "\n--- FULL LOG ---")
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return "\n".join(formatted_parts)
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