Claude researches, Jev decides, you approve

import os, sys, json, datetime
from dotenv import load_dotenv
from anthropic import Anthropic
from typesafe_sdk import Choice, Score, TypeSafeClient


load_dotenv()
# Plain checks, not assert: Python drops asserts when run with -O.
if os.getenv("MODE") != "simulation":
    sys.exit("This guide runs in simulation only.")
if os.getenv("REQUIRE_HUMAN_APPROVAL") != "true":
    sys.exit("REQUIRE_HUMAN_APPROVAL must stay true.")
claude = Anthropic()            # reads ANTHROPIC_API_KEY
jev = TypeSafeClient()          # reads TYPESAFE_API_KEY, calls jev-latest


market_data = input("Paste market data / notes (fictional or your own):\n")
research_prompt = open("prompts/claude_research.txt").read()


msg = claude.messages.create(
    model="claude-opus-5-5", max_tokens=16000,  # thinking counts toward max_tokens
    messages=[{"role": "user", "content": research_prompt + "\n\nMARKET DATA:\n" + market_data}],
)
if msg.stop_reason != "end_turn":  # cut off (max_tokens) or declined (refusal)
    sys.exit(f"Research stopped early ({msg.stop_reason}). Nothing was saved.")
# Thinking is always on in Opus 5.5, so content[0] can be a thinking block: read text by type.
research = "".join(b.text for b in msg.content if b.type == "text")


result = jev.system_one(
    state=research,
    questions={
        "decision": Choice(
            instructions="Based only on this research, choose TRADE only if facts are complete and the thesis is clear; WAIT if a key input is missing; REJECT if evidence conflicts or confidence is low.",
            criteria={"TRADE": "complete, clear, low conflict", "WAIT": "incomplete", "REJECT": "conflicting or weak"},
        ),
        "evidence_quality": Score(
            instructions="How complete and consistent is the evidence?",
            criteria=[
                "Key facts are missing or contradict each other",
                "Most facts are present, with gaps or minor conflicts",
                "Every fact the thesis needs is present and consistent",
            ],
        ),
    },
)
decision = result.choices["decision"]
final = decision.choice
if final == "TRADE" and decision.confidence < 0.7:  # Prompt 2's rule, enforced in code
    final = "REJECT"
record = {
    "time": datetime.datetime.now(datetime.timezone.utc).isoformat(),
    "mode": os.getenv("MODE"),
    "research": research,
    "jev_choice": decision.choice,
    "decision": final,
    "confidence": decision.confidence,
    "probabilities": decision.probabilities,
    "evidence_quality": result.scores["evidence_quality"].score,  # 0 to 2, one step per level
    "human_approval": "PENDING",
}
os.makedirs("decisions", exist_ok=True)
path = f"decisions/{record['time'][:19].replace(':','-')}.json"
with open(path, "w") as f:
    json.dump(record, f, indent=2)
print(json.dumps(record, indent=2))
print(f"\nSaved {path}. AWAITING HUMAN APPROVAL. No order has been or can be placed by this script.")

import os, sys, json, math
from typing import Literal, Optional
from dotenv import load_dotenv
from anthropic import Anthropic
from pydantic import BaseModel, ValidationError


class Levels(BaseModel):
    direction: Literal["long", "short"]
    entry: Optional[float]  # None means NOT IN DATA
    stop: Optional[float]
    target: Optional[float]
    invalidation: str
    reason_to_reject: str


load_dotenv()
if os.getenv("MODE") != "simulation" or os.getenv("REQUIRE_HUMAN_APPROVAL") != "true":
    sys.exit("Simulation with human approval only.")
path = sys.argv[1]  # the decision file run.py saved
with open(path) as f:
    record = json.load(f)
if record["decision"] != "TRADE":
    sys.exit(f"The decision was {record['decision']}, so no plan is produced.")

try:
    msg = Anthropic().messages.parse(
        model="claude-opus-5-5", max_tokens=16000,
        messages=[{"role": "user", "content": (
            "You are the risk desk for a simulation-only study. Using only the research and decision below, "
            "give the direction, the proposed entry, the stop (at or beyond the invalidation level) and the target. "
            "Use prices from the data only, and null for any price that is not in it. Never invent prices. "
            "Describe the invalidation level and give at least one reason to reject the trade."
            f"\n\nDECISION: TRADE at confidence {record['confidence']}\n\nRESEARCH:\n{record['research']}")}],
        output_format=Levels,
    )
except ValidationError:  # a reply cut off or declined doesn't match the schema
    sys.exit("The risk check returned no usable plan. The decision file is unchanged.")
if msg.stop_reason != "end_turn":
    sys.exit(f"Risk check stopped early ({msg.stop_reason}). The decision file is unchanged.")
lv = msg.parsed_output

# The arithmetic stays in code, bounded by your .env, never by the model.
account = float(os.environ["SIM_ACCOUNT_SIZE"])
max_loss = account * float(os.environ["MAX_RISK_PER_TRADE_PCT"]) / 100
plan, problems = lv.model_dump(), []
if None in (lv.entry, lv.stop, lv.target):
    problems.append("a price is NOT IN DATA")
else:
    side = 1 if lv.direction == "long" else -1
    risk = side * (lv.entry - lv.stop)      # loss per unit if the stop is hit
    reward = side * (lv.target - lv.entry)  # gain per unit at the target
    if risk <= 0 or reward <= 0:
        problems.append("the stop or target is on the wrong side of the entry")
    else:
        plan["max_loss"] = round(max_loss, 2)
        plan["size_units"] = math.floor(max_loss / risk)
        plan["reward_to_risk"] = round(reward / risk, 2)
        if plan["reward_to_risk"] < 2:
            problems.append("R:R is below 2")
        if plan["size_units"] < 1:
            problems.append("the stop is too far away for the risk limit")
plan["verdict"] = ("REJECT: " + "; ".join(problems)) if problems else "PLAN READY FOR HUMAN APPROVAL"
record["risk_plan"] = plan
with open(path, "w") as f:
    json.dump(record, f, indent=2)
print(json.dumps(plan, indent=2))