Investment Autoresearch:parse

Autoresearch Parse

Extracts structured JSON from autoresearch markdown outputs for a given ticker, then always runs walk-forward backtests to populate authoritative numeric values.

Two-Phase Process

Phase 1: Parse markdown → text fields (strategy name, insights, rejections, hypotheses)
Phase 2: Run your backtest command → numeric fields (cagr, max_drawdown, walk_forward RA)

Never trust markdown numbers for cagr or max_drawdown. Agents write Return [%] (total return) and Return (Ann.) [%] (CAGR) interchangeably. Only backtesting gives the authoritative annualized CAGR.

Input Files

For a given ticker, two file types exist under archive/{ticker}-autoresearch-v{N}/:

Output JSON Schema

{
  "ticker": "SOXL",
  "research_summary": {
    "rounds_completed": 40,
    "agents_run": 42,
    "strategies_tested": 630
  },
  "current_best": {
    "strategy_name": "R15 W7 + min hold 10 days",
    "parameters": {},
    "cagr": 104.5,
    "max_drawdown": -31.8,
    "sharpe": null,
    "robustness_score": 0.710,
    "walk_forward": {
      "5y": { "cagr": 104.5, "ra": 5.86 },
      "3y": { "cagr": 98.2,  "ra": 5.51 },
      "2y": { "cagr": 110.3, "ra": 6.19 },
      "1y": { "cagr": 88.7,  "ra": 4.98 }
    },
    "min_ra_across_periods": 4.98
  },
  ...
}

cagr and all walk_forward values come from Phase 2 backtesting, not markdown parsing.

Phase 1 — Parse Markdown

ticker

From the verified_insights.md header line: # Verified Insights — {TICKER} ...

research_summary

• rounds_completed: "after N rounds" or count of AGENT_R*_RESULTS.md files
• strategies_tested: "N+ strategies evaluated across N rounds"
• agents_run: same as rounds_completed unless stated otherwise

current_best (text fields only)

Prefer the risk-adjusted / lowest MaxDD champion:

• strategy_name: bolded strategy label
• parameters: extract from markdown if listed; else {}
• Leave cagr, max_drawdown, walk_forward as null — Phase 2 will fill them

leaderboard

From ## Current Best section — ranked strategies with scores. cagr/max_drawdown here come from markdown (acceptable for leaderboard display, but not used for current_best numeric fields).

verified_insights

From ## Confirmed principles — each item → one string. Strip leading number and bold markers.

rejected_approaches

From ## Rejected Approaches. Each → { "approach": "...", "reason": "..." }. Fall back to REJECT rows in AGENT_R*_RESULTS.md if no explicit section.

open_hypotheses

From ## Remaining hypotheses or ## Open Hypotheses.

recommendation

• graduate: strategy name from explicit recommendation or "By risk-adjusted" section
• confidence: derive from walk-forward consistency after Phase 2 completes

Phase 2 — Run Walk-Forward Backtests

Run your configured backtest command across four rolling periods to populate authoritative numeric values. Your backtest command must output lines containing Return (Ann.) [%] and Max. Drawdown [%] — this is the native output format of Backtesting.py; adapt your runner if using a different framework.

Configure your backtest command

In your project, you need a CLI command that:

1. Accepts a ticker and strategy class name as arguments
2. Runs a backtest for the requested period
3. Prints output containing these two lines:

Return (Ann.) [%]     <value>
Max. Drawdown [%]     <value>

Example with Backtesting.py (native format, no adaptation needed):

python backtest_runner.py --ticker SOXL --strategy MyStrategy --period 5y

Example minimal runner if you need to build one:

# backtest_runner.py
import argparse
from backtesting import Backtest
# ... import your strategy ...

parser = argparse.ArgumentParser()
parser.add_argument("--ticker")
parser.add_argument("--strategy")
parser.add_argument("--period", default="5y")
args = parser.parse_args()

# load data, run backtest, print stats
bt = Backtest(data, StrategyClass)
stats = bt.run()
print(stats)  # Backtesting.py prints Return (Ann.) [%] and Max. Drawdown [%] natively

Step 1 — Find the strategy Python class name

Replace <YOUR_STRATEGY_DIR> with your actual strategy directory path (e.g. src/strategies/, strategies/, my_app/trading/):

grep -rn "class.*{strategy_name_keywords}" <YOUR_STRATEGY_DIR>/{ticker}/

Step 2 — Run across four periods

YOUR_BACKTEST_COMMAND --ticker {TICKER} --strategy {ClassName} --period 5y
YOUR_BACKTEST_COMMAND --ticker {TICKER} --strategy {ClassName} --period 3y
YOUR_BACKTEST_COMMAND --ticker {TICKER} --strategy {ClassName} --period 2y
YOUR_BACKTEST_COMMAND --ticker {TICKER} --strategy {ClassName} --period 1y

Parse Return (Ann.) [%] from each run as cagr, and Max. Drawdown [%] as max_drawdown. Compute ra = cagr / abs(max_drawdown) for each period. Set min_ra_across_periods to the minimum RA across all four periods.

Step 3 — Verify the JSON was updated

python3 -c "
import json
with open('archive/{ticker}-autoresearch-v{N}/autoresearch_result.json') as f:
    d = json.load(f)
cb = d['current_best']
print('CAGR:', cb['cagr'])
print('MaxDD:', cb['max_drawdown'])
print('Min RA:', cb['min_ra_across_periods'])
for p, v in cb['walk_forward'].items():
    print(f'  {p}: cagr={v[\"cagr\"]} ra={v[\"ra\"]}')
"

All walk_forward values should be non-null.

Common Mistakes