agent_panel: ATR-normalized candle + indicator-value enrichment, hardened prompts, verdict logging
This commit is contained in:
Harwinder Sekhon
@@ -0,0 +1,116 @@
|
||||
# AI signal enrichment — analysis + build record (2026-08-15)
|
||||
|
||||
Owner questions:
|
||||
1. If the AI panel also received indicator values and (limited) candle data, would the system be more accurate?
|
||||
2. If AI reviewed every candle alongside the local score system, would prediction accuracy justify the token burn — or is there a middle ground?
|
||||
|
||||
## What the panel already received (pre-change baseline)
|
||||
|
||||
The four personas (`agent_panel.py`) already got a *distilled* picture:
|
||||
per-indicator signal+confidence, MTF signals/scores, key levels,
|
||||
exhaustion count, full trade-setup math, warnings, headlines/events/
|
||||
sentiment. Missing: raw indicator **values** and any **candle** data.
|
||||
|
||||
Cost guardrails already in place: 5 requests/panel, `max_panels_per_day`
|
||||
20 (≤100 requests/day), 3600 s reuse-TTL, per-bar exact cache, shared
|
||||
500 req/day DeepSeek cap, static prompts on the ~50× prefix cache.
|
||||
|
||||
## Evidence used
|
||||
|
||||
- **LLM is strong at text, weak at raw numerics** (measured in-repo,
|
||||
`llm_news_eval_2026-08-06.md`): headline sentiment 100% (78/78) vs
|
||||
keyword scorer 31%; but ~11% of event-direction calls misread close
|
||||
numeric comparisons until code pre-computed the comparison
|
||||
(`vs_forecast`) and the model only mapped it to doctrine.
|
||||
- **The book any filter would act on** (M5 canonical,
|
||||
`oanda_m5_12mo_065_nopb_20260814.csv`): 577 trades, +106.82R,
|
||||
PF 1.51, win rate 52.9%; A-grade 59.5%, B-grade 51.4% (+67.8R of the
|
||||
total), C-grade 43.8% (−0.23R, 16 trades). Average edge ≈ 0.185R/trade.
|
||||
|
||||
## Answer 1 — enrich the existing panel? YES (cheap, safe)
|
||||
|
||||
- A compact candle is ~15–25 tokens; 20 primary-TF + 10 confirm-TF bars
|
||||
≈ 500–1,500 tokens per persona call → cents/day at the 20-panel cap.
|
||||
**Tokens are not the constraint; the request cap is.**
|
||||
- Measured after the build (dry-test wire log): enriched technical
|
||||
payload with 20 candles ≈ **~380 tokens**; replies ≈ 25–35 tokens
|
||||
under the new 15-word note cap.
|
||||
- Caveats: (a) the panel is advisory-only — richer payloads improve the
|
||||
human's context, not the book's P&L; (b) raw prices are the model's
|
||||
weakest input, so candles are sent **ATR-normalized** (code does the
|
||||
arithmetic, LLM reads the shape) — the same pattern that fixed the
|
||||
event-direction misreads.
|
||||
|
||||
## Answer 2 — AI on every candle? NO
|
||||
|
||||
- **Feasibility**: M5+M15+H1+H4 ≈ 414 bars/day → one call/bar nearly
|
||||
exhausts the shared 500/day cap (news needs it too); a panel per bar
|
||||
≈ 2,070/day = 4× over. Most of that spend reviews HOLD bars.
|
||||
- **Parity (rule 4)**: an AI gate cannot be replayed by the backtester
|
||||
(news off; no recorded verdicts) — every pinned book becomes
|
||||
non-comparable, and model-version drift silently mutates the strategy.
|
||||
- **Edge**: the engine already compresses 380 bars into 9 tuned
|
||||
indicators + regime + MTF validated over 5 years; an LLM's generic
|
||||
prior on raw candles won't beat fitted thresholds, and the margin it
|
||||
would filter is ~0.185R/trade. B-grade holds +67.8R at 51.4% win —
|
||||
a blunt veto is net-negative.
|
||||
|
||||
## The middle ground (adopted)
|
||||
|
||||
1. **Enrich payloads** (built today, see below) — near-zero cost, no
|
||||
rule-1 exposure.
|
||||
2. **Log panel verdicts to signals.db** (built today) — creates the
|
||||
"does OPPOSE predict losers?" dataset with zero behavior change.
|
||||
3. **After 4–8 weeks**: join logged verdicts to outcomes; simulate an
|
||||
OPPOSE-veto against the pinned book offline. Only then consider any
|
||||
gating, as a full rule-1 revalidation.
|
||||
|
||||
## Build record (2026-08-15, owner-approved)
|
||||
|
||||
- `agent_panel.py`
|
||||
- Technical persona payload now carries indicator **values** (units
|
||||
legend added to the prompt) and **ATR-normalized candles** per TF:
|
||||
`{"ref_close", "atr", "bars": [[o,h,l,c]…]}` oldest-first, each price
|
||||
as `(price − ref_close) / atr`, 1 decimal. Window: 20 bars primary /
|
||||
10 per confirmation TF.
|
||||
- New config keys (validated in `app_config.py`, in
|
||||
`config.json.example`): `candles_enabled` (default true),
|
||||
`candle_bars_primary` (20), `candle_bars_confirm` (10).
|
||||
- Panel-private provider singleton (`_dp`, patchable in tests);
|
||||
TTL cache keeps fetches near-free; any failure → candles silently
|
||||
omitted, panel unaffected.
|
||||
- Prompt hardening: training data declared stale and OFF-LIMITS;
|
||||
"if a fact is not in the JSON, treat it as unknown"; thin evidence
|
||||
→ neutral < 30 (unchanged); note cap 25→**15 words**, rationale
|
||||
40→**30 words** (output-token savings).
|
||||
- `attach_panel` now calls `signal_log.log_panel_update(report)` after
|
||||
every successful attach (fresh, exact-cache, and reuse-TTL paths).
|
||||
- `signal_log.py`
|
||||
- `log()` returns the rowid; `log_signal` stashes it on the report as
|
||||
`_signal_log_rowid`; new `log_panel_update()` fills the 4 panel
|
||||
columns on that row post-hoc. No rowid (backtest replay, logging
|
||||
disabled, other process) → silent no-op. Fail-safe contract kept.
|
||||
- Tests: 381 → **388 passed** (7 new: candle normalization math, payload
|
||||
values/candles, context windows + ATR gating + disable flag, attach
|
||||
injection (technical persona only), prompt-contract assertions,
|
||||
panel row update, rowid hand-off + no-op paths). Existing attach tests
|
||||
stub `ap._dp` to stay off the network.
|
||||
- **Live dry test** (`panel_payload_drytest_2026-08-15.py`, fabricated
|
||||
numbers only, 13 real DeepSeek requests total): **7/7 probes pass** —
|
||||
coherent-bullish → support@70; bearish-vs-BUY → oppose@85; genuinely
|
||||
mixed → neutral@30; empty payload → neutral@20 (contract); no invented
|
||||
numbers in any note; all notes ≤ 15 words; synthesizer AGREE with a
|
||||
21-word rationale. Raw wire log:
|
||||
`backtest/research/panel_payload_drytest_2026-08-15.jsonl`.
|
||||
- Notable: an early P3 variant with H1 SELL against a BUY brief got
|
||||
oppose@60 — *correct* per the prompt (higher-TF disagreement is a
|
||||
listed conflict), so the probe was tightened to a genuinely mixed
|
||||
scenario. Documented so nobody "fixes" the prompt for it later.
|
||||
|
||||
## What this deliberately did NOT change
|
||||
|
||||
- No gate reads panel fields; the panel remains advisory-only.
|
||||
- No backtest-path change (`analyze_from_dataframes` untouched; parity
|
||||
test still locks panel fields empty in replay).
|
||||
- No verdict-based filtering — that decision waits for the signals.db
|
||||
dataset (4–8 weeks) and an offline veto replay against the pinned book.
|
||||
@@ -0,0 +1,7 @@
|
||||
{"probe": "P1 coherent-bullish", "payload": {"brief": {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY", "quality": "A", "score": 38.5, "confidence": 62, "price": 2000.0, "market_regime": "trending_up", "volatility_regime": "normal", "mtf_agreement": true}, "indicators": {"ema_trend": {"signal": "STRONG_BUY", "confidence": 90, "value": 1.0}, "rsi": {"signal": "BUY", "confidence": 65, "value": 58.0}, "macd": {"signal": "STRONG_BUY", "confidence": 80, "value": 1.25}, "bollinger": {"signal": "BUY", "confidence": 60, "value": 0.7}, "adx": {"signal": "BUY", "confidence": 70, "value": 32.0}, "roc": {"signal": "BUY", "confidence": 55, "value": 0.35}, "keltner": {"signal": "BUY", "confidence": 65, "value": 3.1}, "candlestick": {"signal": "BUY", "confidence": 60, "value": 2.0}}, "mtf": {"5min": {"signal": "BUY", "score": 30.0}, "1h": {"signal": "STRONG_BUY", "score": 45.0}}, "key_levels": {"resistance": 2012.0, "support": 1985.0}, "exhaustion": 0, "candles": {"15min": {"ref_close": 2000.0, "atr": 5.0, "bars": [[-2.4, -2.2, -2.4, -2.3], [-2.3, -2.1, -2.3, -2.2], [-2.2, -2.0, -2.2, -2.0], [-2.0, -1.9, -2.1, -1.9], [-1.9, -1.8, -2.0, -1.8], [-1.8, -1.6, -1.8, -1.7], [-1.7, -1.5, -1.7, -1.6], [-1.6, -1.4, -1.6, -1.4], [-1.4, -1.3, -1.5, -1.3], [-1.3, -1.2, -1.4, -1.2], [-1.2, -1.0, -1.2, -1.1], [-1.1, -0.9, -1.1, -1.0], [-1.0, -0.8, -1.0, -0.8], [-0.8, -0.7, -0.9, -0.7], [-0.7, -0.6, -0.8, -0.6], [-0.6, -0.4, -0.6, -0.5], [-0.5, -0.3, -0.5, -0.4], [-0.4, -0.2, -0.4, -0.2], [-0.2, -0.1, -0.3, -0.1], [-0.1, 0.0, -0.2, 0.0]]}}}, "raw_reply": {"stance": "support", "confidence": 70, "note": "The trend is up and all signs agree, so buying fits the flow."}}
|
||||
{"probe": "P2 bearish-vs-BUY-brief", "payload": {"brief": {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY", "quality": "A", "score": 38.5, "confidence": 62, "price": 2000.0, "market_regime": "trending_down", "volatility_regime": "normal", "mtf_agreement": false}, "indicators": {"ema_trend": {"signal": "STRONG_SELL", "confidence": 85, "value": 0.1}, "rsi": {"signal": "SELL", "confidence": 70, "value": 74.0}, "macd": {"signal": "STRONG_SELL", "confidence": 80, "value": -1.4}, "bollinger": {"signal": "SELL", "confidence": 60, "value": 0.95}, "adx": {"signal": "SELL", "confidence": 70, "value": 35.0}, "roc": {"signal": "SELL", "confidence": 60, "value": -0.5}, "keltner": {"signal": "SELL", "confidence": 65, "value": -2.8}, "candlestick": {"signal": "SELL", "confidence": 55, "value": -2.0}}, "mtf": {"5min": {"signal": "SELL", "score": -28.0}, "1h": {"signal": "STRONG_SELL", "score": -41.0}}, "key_levels": {"resistance": 2012.0, "support": 1985.0}, "exhaustion": 3, "candles": {"15min": {"ref_close": 2000.0, "atr": 5.0, "bars": [[2.4, 2.4, 2.2, 2.3], [2.3, 2.3, 2.1, 2.2], [2.2, 2.2, 2.0, 2.0], [2.0, 2.1, 1.9, 1.9], [1.9, 2.0, 1.8, 1.8], [1.8, 1.8, 1.6, 1.7], [1.7, 1.7, 1.5, 1.6], [1.6, 1.6, 1.4, 1.4], [1.4, 1.5, 1.3, 1.3], [1.3, 1.4, 1.2, 1.2], [1.2, 1.2, 1.0, 1.1], [1.1, 1.1, 0.9, 1.0], [1.0, 1.0, 0.8, 0.8], [0.8, 0.9, 0.7, 0.7], [0.7, 0.8, 0.6, 0.6], [0.6, 0.6, 0.4, 0.5], [0.5, 0.5, 0.3, 0.4], [0.4, 0.4, 0.2, 0.2], [0.2, 0.3, 0.1, 0.1], [0.1, 0.2, -0.0, 0.0]]}}}, "raw_reply": {"stance": "oppose", "confidence": 85, "note": "The price is falling and every indicator says sell, so buying fights the trend."}}
|
||||
{"probe": "P3 conflicting", "payload": {"brief": {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY", "quality": "A", "score": 12.0, "confidence": 35, "price": 2000.0, "market_regime": "ranging", "volatility_regime": "normal", "mtf_agreement": false}, "indicators": {"ema_trend": {"signal": "BUY", "confidence": 55, "value": 0.6}, "rsi": {"signal": "NEUTRAL", "confidence": 40, "value": 51.0}, "macd": {"signal": "SELL", "confidence": 55, "value": -0.3}, "bollinger": {"signal": "NEUTRAL", "confidence": 35, "value": 0.5}, "adx": {"signal": "NEUTRAL", "confidence": 40, "value": 18.0}, "roc": {"signal": "SELL", "confidence": 50, "value": -0.1}, "keltner": {"signal": "BUY", "confidence": 50, "value": 0.8}, "candlestick": {"signal": "NEUTRAL", "confidence": 30, "value": 0.0}}, "mtf": {"5min": {"signal": "NEUTRAL", "score": 4.0}, "1h": {"signal": "NEUTRAL", "score": -3.0}}, "key_levels": {}, "exhaustion": 0, "candles": {"15min": {"ref_close": 2000.0, "atr": 5.0, "bars": [[0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0], [0.0, 0.4, -0.1, 0.3], [0.3, 0.4, -0.1, 0.0]]}}}, "raw_reply": {"stance": "neutral", "confidence": 30, "note": "Mixed signals with no clear direction, so cannot strongly support or oppose."}}
|
||||
{"probe": "P4 empty-evidence", "payload": {"brief": {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY", "quality": "A", "score": 38.5, "confidence": 62, "price": 2000.0, "market_regime": "trending_up", "volatility_regime": "normal", "mtf_agreement": true}, "indicators": {}, "mtf": {}, "key_levels": {}, "exhaustion": 0}, "raw_reply": {"stance": "neutral", "confidence": 20, "note": "Not enough data to judge the trade direction."}}
|
||||
{"probe": "P5 hallucination-trap", "payload": {"brief": {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY", "quality": "A", "score": 38.5, "confidence": 62, "price": 2000.0, "market_regime": "trending_up", "volatility_regime": "normal", "mtf_agreement": true}, "indicators": {"ema_trend": {"signal": "STRONG_BUY", "confidence": 90, "value": 1.0}, "rsi": {"signal": "BUY", "confidence": 65, "value": 58.0}, "macd": {"signal": "STRONG_BUY", "confidence": 80, "value": 1.25}, "bollinger": {"signal": "BUY", "confidence": 60, "value": 0.7}, "adx": {"signal": "BUY", "confidence": 70, "value": 32.0}, "roc": {"signal": "BUY", "confidence": 55, "value": 0.35}, "keltner": {"signal": "BUY", "confidence": 65, "value": 3.1}, "candlestick": {"signal": "BUY", "confidence": 60, "value": 2.0}}, "mtf": {"5min": {"signal": "BUY", "score": 30.0}, "1h": {"signal": "STRONG_BUY", "score": 45.0}}, "key_levels": {}, "exhaustion": 0}, "raw_reply": {"stance": "support", "confidence": 70, "note": "The trend is up and all signs agree, so buying fits the flow."}}
|
||||
{"probe": "P6 output-discipline", "payload": {}, "raw_reply": null}
|
||||
{"probe": "P7 synthesizer", "payload": {"direction": "BUY", "takes": [{"persona": "macro", "stance": "support", "confidence": 70, "note": "evidence aligned"}, {"persona": "technical", "stance": "support", "confidence": 80, "note": "evidence aligned"}, {"persona": "risk", "stance": "support", "confidence": 65, "note": "evidence aligned"}, {"persona": "contrarian", "stance": "support", "confidence": 60, "note": "evidence aligned"}]}, "raw_reply": {"verdict": "AGREE", "confidence": 85, "rationale": "All four analysts support buying, with high confidence and consistent evidence. No one opposes, so the panel clearly backs the trade."}}
|
||||
@@ -0,0 +1,271 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
panel_payload_drytest_2026-08-15.py — live DeepSeek validation of the
|
||||
enriched agent-panel payloads + hardened prompts (2026-08-15).
|
||||
|
||||
Spends ~6 REAL DeepSeek requests (against the shared 500/day cap) with
|
||||
FABRICATED payloads — made-up prices/indicators, no real market data —
|
||||
to verify the new technical-persona contract end to end:
|
||||
|
||||
P1 coherent bullish evidence -> technical persona SUPPORTS
|
||||
P2 bearish evidence vs BUY brief -> technical persona OPPOSES
|
||||
P3 conflicting evidence -> NEUTRAL or low confidence
|
||||
P4 empty evidence -> NEUTRAL with confidence < 30
|
||||
(the anti-hallucination contract)
|
||||
P5 hallucination trap -> note invents no price levels
|
||||
P6 output discipline -> <=20-word notes, exact JSON keys
|
||||
P7 synthesizer over 4 supports -> AGREE verdict
|
||||
|
||||
Usage: venv/bin/python backtest/research/panel_payload_drytest_2026-08-15.py
|
||||
Exit code 0 = all probes passed. Raw replies land in
|
||||
backtest/research/panel_drytest_2026-08-15.jsonl
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[2]
|
||||
sys.path.insert(0, str(REPO_ROOT))
|
||||
|
||||
import agent_panel as ap # noqa: E402
|
||||
from app_config import deepseek_api_key, get_app_config # noqa: E402
|
||||
from llm_client import get_llm_client # noqa: E402
|
||||
|
||||
ARTIFACT = Path(__file__).with_suffix(".jsonl")
|
||||
|
||||
|
||||
# ── Fabrication helpers (all numbers are invented) ───────────────────
|
||||
|
||||
def _staircase(start: float, step: float, n: int) -> pd.DataFrame:
|
||||
"""n synthetic bars drifting `step` per bar; last close = start+n*step."""
|
||||
rows, price = [], start
|
||||
for _ in range(n):
|
||||
o, c = price, price + step
|
||||
rows.append({"Open": o, "Close": c,
|
||||
"High": max(o, c) + abs(step) * 0.4,
|
||||
"Low": min(o, c) - abs(step) * 0.4})
|
||||
price = c
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
def _choppy(center: float, n: int) -> pd.DataFrame:
|
||||
rows, price, sign = [], center, 1
|
||||
for _ in range(n):
|
||||
step = sign * 1.5
|
||||
o, c = price, price + step
|
||||
rows.append({"Open": o, "Close": c,
|
||||
"High": max(o, c) + 0.6, "Low": min(o, c) - 0.6})
|
||||
price, sign = c, -sign
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
_FAKE_PRICE = 2000.00 # deliberately made-up
|
||||
_FAKE_ATR = 5.0
|
||||
|
||||
_BRIEF_BUY = {"pair": "XAU/USD", "timeframe": "15min", "direction": "BUY",
|
||||
"quality": "A", "score": 38.5, "confidence": 62,
|
||||
"price": _FAKE_PRICE, "market_regime": "trending_up",
|
||||
"volatility_regime": "normal", "mtf_agreement": True}
|
||||
|
||||
_BULL_INDICATORS = {
|
||||
"ema_trend": {"signal": "STRONG_BUY", "confidence": 90, "value": 1.0},
|
||||
"rsi": {"signal": "BUY", "confidence": 65, "value": 58.0},
|
||||
"macd": {"signal": "STRONG_BUY", "confidence": 80, "value": 1.25},
|
||||
"bollinger": {"signal": "BUY", "confidence": 60, "value": 0.7},
|
||||
"adx": {"signal": "BUY", "confidence": 70, "value": 32.0},
|
||||
"roc": {"signal": "BUY", "confidence": 55, "value": 0.35},
|
||||
"keltner": {"signal": "BUY", "confidence": 65, "value": 3.1},
|
||||
"candlestick": {"signal": "BUY", "confidence": 60, "value": 2.0},
|
||||
}
|
||||
|
||||
_BEAR_INDICATORS = {
|
||||
"ema_trend": {"signal": "STRONG_SELL", "confidence": 85, "value": 0.1},
|
||||
"rsi": {"signal": "SELL", "confidence": 70, "value": 74.0},
|
||||
"macd": {"signal": "STRONG_SELL", "confidence": 80, "value": -1.4},
|
||||
"bollinger": {"signal": "SELL", "confidence": 60, "value": 0.95},
|
||||
"adx": {"signal": "SELL", "confidence": 70, "value": 35.0},
|
||||
"roc": {"signal": "SELL", "confidence": 60, "value": -0.5},
|
||||
"keltner": {"signal": "SELL", "confidence": 65, "value": -2.8},
|
||||
"candlestick": {"signal": "SELL", "confidence": 55, "value": -2.0},
|
||||
}
|
||||
|
||||
_MIXED_INDICATORS = {
|
||||
"ema_trend": {"signal": "BUY", "confidence": 55, "value": 0.6},
|
||||
"rsi": {"signal": "NEUTRAL", "confidence": 40, "value": 51.0},
|
||||
"macd": {"signal": "SELL", "confidence": 55, "value": -0.3},
|
||||
"bollinger": {"signal": "NEUTRAL", "confidence": 35, "value": 0.5},
|
||||
"adx": {"signal": "NEUTRAL", "confidence": 40, "value": 18.0},
|
||||
"roc": {"signal": "SELL", "confidence": 50, "value": -0.1},
|
||||
"keltner": {"signal": "BUY", "confidence": 50, "value": 0.8},
|
||||
"candlestick": {"signal": "NEUTRAL", "confidence": 30, "value": 0.0},
|
||||
}
|
||||
|
||||
|
||||
def _tech_payload(indicators, mtf, candles_df=None, key_levels=None,
|
||||
brief=None, exhaustion=0):
|
||||
payload = {"brief": brief or dict(_BRIEF_BUY),
|
||||
"indicators": indicators, "mtf": mtf,
|
||||
"key_levels": key_levels or {}, "exhaustion": exhaustion}
|
||||
if candles_df is not None:
|
||||
# the REAL production normalizer, not a hand-rolled copy
|
||||
payload["candles"] = {
|
||||
"15min": ap._normalize_candles(candles_df, _FAKE_ATR,
|
||||
len(candles_df))}
|
||||
return payload
|
||||
|
||||
|
||||
# ── Probe runner ─────────────────────────────────────────────────────
|
||||
|
||||
_RESULTS = []
|
||||
|
||||
|
||||
def _record(probe, payload, reply, take, passed, detail):
|
||||
_RESULTS.append({"probe": probe, "pass": bool(passed),
|
||||
"detail": detail,
|
||||
"stance": getattr(take, "stance", None),
|
||||
"confidence": getattr(take, "confidence", None),
|
||||
"note": getattr(take, "note", None)})
|
||||
with ARTIFACT.open("a") as fh:
|
||||
fh.write(json.dumps({"probe": probe, "payload": payload,
|
||||
"raw_reply": reply}, default=str) + "\n")
|
||||
mark = "PASS" if passed else "FAIL"
|
||||
print(f"[{mark}] {probe}: {detail}")
|
||||
if take is not None:
|
||||
print(f" -> {take.stance} @ {take.confidence:.0f} "
|
||||
f"| {take.note}")
|
||||
|
||||
|
||||
def _ask(payload):
|
||||
"""Real API call through the production client + production
|
||||
validation (the same path run_panel uses)."""
|
||||
reply = get_llm_client().chat_json(ap._TECH_SYSTEM, payload)
|
||||
return reply, ap._validate_take("technical", reply)
|
||||
|
||||
|
||||
def _want(probe: str) -> bool:
|
||||
"""--only P3 runs just that probe (saves requests on re-runs)."""
|
||||
if "--only" not in sys.argv:
|
||||
return True
|
||||
return sys.argv[sys.argv.index("--only") + 1] == probe
|
||||
|
||||
|
||||
def main() -> int:
|
||||
app = get_app_config()
|
||||
if not deepseek_api_key(app):
|
||||
print("ABORT: no DeepSeek API key (env DEEPSEEK_API_KEY / "
|
||||
"config.json deepseek.api_key).")
|
||||
return 2
|
||||
if app.deepseek_settings.get("enabled", True) is False:
|
||||
print("ABORT: deepseek.enabled is false in config.json.")
|
||||
return 2
|
||||
if ARTIFACT.exists() and "--only" not in sys.argv:
|
||||
ARTIFACT.unlink() # full run = fresh wire log; --only appends
|
||||
|
||||
mtf_agree = {"5min": {"signal": "BUY", "score": 30.0},
|
||||
"1h": {"signal": "STRONG_BUY", "score": 45.0}}
|
||||
levels = {"resistance": 2012.0, "support": 1985.0}
|
||||
|
||||
# P1 — coherent bullish, with rising ATR staircase (last close 2000)
|
||||
if _want("P1"):
|
||||
p1 = _tech_payload(_BULL_INDICATORS, mtf_agree,
|
||||
candles_df=_staircase(_FAKE_PRICE - 20 * 0.6,
|
||||
0.6, 20),
|
||||
key_levels=levels)
|
||||
reply, take = _ask(p1)
|
||||
_record("P1 coherent-bullish", p1, reply, take,
|
||||
take is not None and take.stance == "support"
|
||||
and take.confidence >= 50,
|
||||
"expect support @ conf>=50")
|
||||
|
||||
# P2 — bearish everything against a BUY brief, falling staircase
|
||||
if _want("P2"):
|
||||
brief2 = dict(_BRIEF_BUY, market_regime="trending_down",
|
||||
mtf_agreement=False)
|
||||
mtf_sell = {"5min": {"signal": "SELL", "score": -28.0},
|
||||
"1h": {"signal": "STRONG_SELL", "score": -41.0}}
|
||||
p2 = _tech_payload(_BEAR_INDICATORS, mtf_sell, brief=brief2,
|
||||
candles_df=_staircase(_FAKE_PRICE + 20 * 0.6,
|
||||
-0.6, 20),
|
||||
key_levels=levels, exhaustion=3)
|
||||
reply, take = _ask(p2)
|
||||
_record("P2 bearish-vs-BUY-brief", p2, reply, take,
|
||||
take is not None and take.stance == "oppose",
|
||||
"expect oppose (BUY brief fights all evidence)")
|
||||
|
||||
# P3 — genuinely mixed evidence: MTF neutral both ways, ranging
|
||||
# regime, choppy candles. (An H1 SELL against a BUY is a *listed
|
||||
# conflict* in the prompt — oppose is correct there, not mixed.)
|
||||
if _want("P3"):
|
||||
brief3 = dict(_BRIEF_BUY, market_regime="ranging", score=12.0,
|
||||
confidence=35, mtf_agreement=False)
|
||||
mtf_mixed = {"5min": {"signal": "NEUTRAL", "score": 4.0},
|
||||
"1h": {"signal": "NEUTRAL", "score": -3.0}}
|
||||
p3 = _tech_payload(_MIXED_INDICATORS, mtf_mixed, brief=brief3,
|
||||
candles_df=_choppy(_FAKE_PRICE, 20))
|
||||
reply, take = _ask(p3)
|
||||
_record("P3 conflicting", p3, reply, take,
|
||||
take is not None and (take.stance == "neutral"
|
||||
or take.confidence < 60),
|
||||
"expect neutral or conf<60 on genuinely mixed evidence")
|
||||
|
||||
# P4 — empty evidence: contract demands neutral @ conf<30
|
||||
if _want("P4"):
|
||||
p4 = _tech_payload({}, {}, key_levels=None)
|
||||
reply, take = _ask(p4)
|
||||
_record("P4 empty-evidence", p4, reply, take,
|
||||
take is not None and take.stance == "neutral"
|
||||
and take.confidence < 30,
|
||||
"contract: thin/missing evidence -> neutral, conf<30")
|
||||
|
||||
# P5 — hallucination trap: no key_levels, no candles; the note must
|
||||
# not cite any 3+-digit number absent from the payload
|
||||
if _want("P5"):
|
||||
p5 = _tech_payload(_BULL_INDICATORS, mtf_agree, key_levels=None)
|
||||
reply, take = _ask(p5)
|
||||
invented = []
|
||||
if take is not None:
|
||||
payload_blob = json.dumps(p5)
|
||||
for num in re.findall(r"\d{3,}", take.note):
|
||||
if num not in payload_blob:
|
||||
invented.append(num)
|
||||
_record("P5 hallucination-trap", p5, reply, take,
|
||||
take is not None and not invented,
|
||||
f"invented numbers in note: {invented or 'none'}")
|
||||
|
||||
# P6 — output discipline across the persona replies gathered above
|
||||
notes = [r for r in _RESULTS if r["note"]]
|
||||
wordy = [r["probe"] for r in notes if len(r["note"].split()) > 20]
|
||||
_record("P6 output-discipline", {}, None, None,
|
||||
notes and not wordy,
|
||||
f"notes >20 words: {wordy or 'none'} "
|
||||
f"(cap is 15; 20 = fail threshold)")
|
||||
|
||||
# P7 — synthesizer over four fabricated SUPPORT takes
|
||||
if _want("P7"):
|
||||
takes = [{"persona": p, "stance": "support", "confidence": c,
|
||||
"note": "evidence aligned"}
|
||||
for p, c in (("macro", 70), ("technical", 80),
|
||||
("risk", 65), ("contrarian", 60))]
|
||||
synth_payload = {"direction": "BUY", "takes": takes}
|
||||
reply = get_llm_client().chat_json(ap._SYNTH_SYSTEM, synth_payload)
|
||||
verdict = reply.get("verdict") if isinstance(reply, dict) else None
|
||||
rationale = (reply or {}).get("rationale", "")
|
||||
_record("P7 synthesizer", synth_payload, reply, None,
|
||||
verdict == "AGREE" and len(str(rationale).split()) <= 40,
|
||||
f"expect AGREE + <=40-word rationale; got {verdict!r} "
|
||||
f"({len(str(rationale).split())} words)")
|
||||
|
||||
n_pass = sum(1 for r in _RESULTS if r["pass"])
|
||||
n_api = sum(1 for r in _RESULTS if r["probe"] != "P6 output-discipline")
|
||||
print(f"\n{n_pass}/{len(_RESULTS)} probes passed "
|
||||
f"({n_api} API requests spent; "
|
||||
f"raw wire log: {ARTIFACT.name})")
|
||||
return 0 if n_pass == len(_RESULTS) else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user