Dawn Proactive Agent v1.5

Dawn Agent v1.5 self-evolution proactive architecture. P0-P4 framework for autonomous ETF trading agent with self-reflection, state machine, audit trail, multi-dimension scoring, and safety guardrails.

An autonomous, self-evolving ETF trading agent architecture designed for the A-share market.

Architecture Overview

text

┌─────────────────────────────────────────────────────┐
│              Dawn Proactive Agent v1.5              │
├─────────────────────────────────────────────────────┤
│  P0: dawn_reflect.py    Hermes-inspired reflection  │
│  P1: dawn_state.py      LangGraph state machine     │
│  P2: dawn_audit.py      RagaAI audit trail          │
│  P3: dawn_analysis.py   ai-berkshire 4D scoring     │
│  P4: dawn_guardrails.py OpenAI Agents safety        │
├─────────────────────────────────────────────────────┤
│       Active in: 华泰柏瑞杯ETF AI交易巅峰赛          │
│       Period: 2026/6/11 - 2026/7/20                 │
│       Portfolio: 8 ETFs, ~¥1M AUM                    │
└─────────────────────────────────────────────────────┘

Components

P0 - Self-Reflection Engine (dawn_reflect.py)

Hermes-inspired post-trade reflection. After every portfolio adjustment, automatically extracts lessons learned and loads them into the next decision cycle.

• Trigger: post-trade callback
• Output: structured learnings with recall scoring
• Integrates with .learnings/ structured log system

P1 - State Machine (dawn_state.py)

LangGraph-inspired workflow orchestration with checkpoint/resume.

• States: IDLE → ANALYZE → DECIDE → EXECUTE → REFLECT → IDLE
• Checkpoint recovery on timeout/crash
• Timeout downgrade: auto-fallback to safest state

P2 - Audit Trail (dawn_audit.py)

RagaAI-inspired immutable decision logging.

• Every decision recorded: timestamp, reasoning, data sources, signals, outcome
• Queryable by date/strategy/symbol
• Exports as structured JSON for backtesting

P3 - Multi-Dimension Scoring (dawn_analysis.py)

ai-berkshire-inspired composite scoring for ETF selection.

• Technical (30%): momentum, trend, volume
• News Sentiment (25%): real-time financial news analysis
• Capital Flow (25%): sector money flow tracking
• Volume-Price (20%): volume-price divergence detection

P4 - Safety Guardrails (dawn_guardrails.py)

OpenAI Agents SDK-inspired transaction safety checks.

• Blacklist: 科创板(688) and ST stocks blocked
• Position limits: max 40% single ETF
• Daily loss limit: max -5% stop-loss
• Available cash check before execution

Usage

Daily Strategy Run (09:28)

bash

python scripts/dawn_proactive.py --action morning

Post-Market Review (15:05)

bash

python scripts/dawn_proactive.py --action afternoon

Midday Check (11:30)

bash

python scripts/dawn_proactive.py --action midday

Manual Trade

bash

python scripts/dawn_etf_rotator.py --execute

Files

File	Purpose
scripts/dawn_proactive.py	Main orchestrator
scripts/dawn_etf_rotator.py	ETF rotation strategy
scripts/dawn_reflect.py	P0: Self-reflection
scripts/dawn_state.py	P1: State machine
scripts/dawn_audit.py	P2: Audit trail
scripts/dawn_analysis.py	P3: 4D scoring
scripts/dawn_guardrails.py	P4: Safety guardrails
scripts/dawn_memory_sync.py	L1↔L3 memory sync
scripts/dawn_collector.py	Market data collection
scripts/dawn_selector.py	ETF selection logic
scripts/dawn_monitor.py	Real-time monitoring

Results (2026-07-06)

• Cron jobs: 10 automated tasks running daily
• Portfolio: 8 ETFs, 38.2%仓位, ¥1,003,751
• Key wins: 科创板V反守住(+0.69% on 07-03), 全天候自动运行
• Self-healing: 收盘复盘cron timeout自动修复 (agentTurn→command模式)
• Delivery: 飞书推送自动修复 (delivery.to user:前缀)

See samples/proactive_demo.md for a full session trace.

Changelog

v1.5 (2026-07-06)

• P0-P4 framework complete
• Cron self-healing: timeout 120s→300s, agentTurn→command mode
• Delivery fix: feishu user: prefix
• Git cleanup: 380K lines of old archive deleted

v1.0 (2026-06-24)

• Initial dawn agent framework
• LM Studio + DeepSeek-R1 local inference
• Memory sync and knowledge injection