improve: 增强进度反馈，捕获TradingAgents实时输出

- 捕获stdout输出同步到前端日志 - 添加等待提示（3-8分钟） - 过滤有意义的进度信息
fix: 缓存加载后Date列转datetime，修复类型比较错误
2026-07-08 17:36:39 +08:00 · 2026-07-08 17:21:33 +08:00 · 2026-07-08 17:15:16 +08:00 · 2026-07-08 17:05:13 +08:00 · 2026-07-08 14:38:57 +08:00 · 2026-07-08 14:34:41 +08:00
39 changed files with 2365 additions and 135 deletions
@@ -25,8 +25,10 @@ NVIDIA_API_KEY=
 #OPENAI_COMPATIBLE_API_KEY=
 # AWS Bedrock (provider "bedrock", install with: pip install ".[bedrock]").
-# Auth uses the standard AWS credential chain; set the region (and optionally a
+# Auth: either a Bedrock API key (bearer token, no AWS access keys) OR the AWS
-# named profile). No single API key.
+# credential chain (env keys / ~/.aws/credentials / IAM role / AWS_PROFILE). Set
 # the region either way; a bearer token takes precedence when both are present.
 #AWS_BEARER_TOKEN_BEDROCK=
 #AWS_DEFAULT_REGION=us-west-2
 #AWS_PROFILE=
@@ -55,6 +57,10 @@ NVIDIA_API_KEY=
 # honor it). Unset leaves each provider at its default. See the README
 # "Reproducibility" note — no setting makes LLM output fully deterministic.
 #TRADINGAGENTS_TEMPERATURE=0.0
 # LLM SDK retry budget forwarded to every provider. Unset leaves each SDK at its
 # own default (usually 2). Raise it to ride out bursty 429 rate-limit throttling
 # on rate-limited deployments (e.g. Azure OpenAI) instead of aborting the run.
 #TRADINGAGENTS_LLM_MAX_RETRIES=6
 # Provider-specific reasoning/thinking depth (optional; unset = provider
 # default). Setting one also skips the matching interactive prompt.
 #TRADINGAGENTS_OPENAI_REASONING_EFFORT=medium
@@ -6,6 +6,107 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 Breaking changes within the 0.x line are called out explicitly.
 ## [0.3.1] — 2026-07-05
 Correctness and stability patch: data look-ahead, graph-router crash-safety,
 checkpoint identity, crypto sentiment sources, and configurable resilience.
 ### Fixed
 - **Alpha Vantage look-ahead filter now runs.** The fundamentals payload is a
  JSON string, so the dict-only guard skipped filtering and future-dated reports
  leaked into historical runs; parse before filtering. (#1115, @zachthebird)
 - **News analyst prompt matches the tool.** The prompt advertised
  `get_news(query, ...)` but the tool takes a ticker; aligned to stop
  hallucinated free-text query calls. (#1116, @shcheuk)
 - **Shared debate/risk routers can't crash mid-run.** Both routers return more
  targets than any one edge mapped; every edge now shares the complete path map,
  so a fall-through under prompt/i18n/refactor drift stays routable.
  (#1088, @Fr3ya, @sa7an7, @Sushanth012)
 - **Checkpoint resume respects graph shape.** The thread id folds in selected
  analysts, debate/risk depth, and asset mode, so a resume under different
  choices no longer continues the wrong graph. (#1089, @bossjoker1, @Ghraven)
 - **Crypto sentiment sources resolve.** StockTwits lists crypto as `<BASE>.X`
  (Yahoo's `BTC-USD` 404s) and Reddit needs the base symbol to match; the social
  path now maps crypto correctly for both. (#1113, @suremadoreai)
 ### Added
 - **Configurable LLM retry budget.** `llm_max_retries` /
  `TRADINGAGENTS_LLM_MAX_RETRIES` is forwarded to every provider, so a transient
  429 burst no longer aborts a run. (#1091, @yanggaome)
 - **Bedrock API-key auth.** `AWS_BEARER_TOKEN_BEDROCK` authenticates Amazon
  Bedrock without AWS access keys and takes precedence over an ambient
  `AWS_PROFILE`. (#1103, @praxstack)
 - **Latest Claude models.** Added Claude Sonnet 5 (`claude-sonnet-5`) and
  Fable 5 (`claude-fable-5`); effort control now covers the Claude 5 line.
 ## [0.3.0] — 2026-06-22
 Stabilization and extensibility release: a CI gate, a unified verified
 data-access contract, a provider and data-vendor registry, and a maintenance
 sweep that hardened config precedence, the model catalog, data resilience, and
 structured output.
 ### Added
 - **CI gate.** GitHub Actions runs the pytest suite across Python 3.10-3.13,
  strict `ruff`, and a clean-install smoke that imports the package and CLI to
  catch undeclared dependencies. (#994, #197)
 - **Provider registry.** OpenAI-compatible providers register as a single spec,
  and a generic `openai_compatible` endpoint covers vLLM, LM Studio, and relays.
  Adds NVIDIA NIM, Kimi, Groq, Mistral, and a native Amazon Bedrock client.
 - **Macro and prediction-market vendors.** FRED macro indicators and Polymarket
  event probabilities, surfaced to the news and macro analysts.
 - **Programmatic report output.** `TradingAgentsGraph.save_reports()` writes the
  same report tree the CLI produces, for headless and API runs. (#1037)
 - **Env-configurable reasoning depth** via `TRADINGAGENTS_OPENAI_REASONING_EFFORT`,
  `TRADINGAGENTS_GOOGLE_THINKING_LEVEL`, and `TRADINGAGENTS_ANTHROPIC_EFFORT`,
  each gated to the models that accept it.
 ### Changed
 - **Verified data-access contract.** Symbol normalization on every vendor path
  (identity, returns, CLI, news); the configured vendor list is the exact
  resolution chain with no silent fallback to unselected vendors; a typed
  `VendorError` taxonomy; look-ahead-safe news windows; stale-OHLCV rejection;
  inclusive yfinance date ranges.
 - **Config precedence.** An explicit `TRADINGAGENTS_*` value or CLI flag now wins
  over interactive defaults for debate and risk round counts,
  `--checkpoint / --no-checkpoint`, and the Docker provider profile; invalid
  boolean env values fail loudly. (#975, #976, #977)
 - **Current-generation model catalog.** Refreshed provider lineups; retired
  `gpt-4.1`, Claude Sonnet 4.5, and the Gemini 2.5 line.
 - **Optional vendors degrade** instead of aborting a run: a failed macro or
  prediction-market lookup returns a no-data sentinel.
 - **Analyst prompts lead with the current date** so tool-call date ranges anchor
  to the run date rather than the model's training cutoff. (#836)
 ### Fixed
 - **Instrument identity.** Deterministic ticker-to-company resolution prevents
  wrong-company hallucination, and a verified market-data snapshot grounds price
  and indicator claims. (#814, #830)
 - **Social and market data sources.** Reddit RSS-first with 429 backoff,
  StockTwits transport hardening, and Alpha Vantage timeout plus
  key-versus-rate-limit handling.
 - **Structured output.** Local OpenAI-compatible servers no longer reject
  object-form `tool_choice`; a thinking model that returns no parsed result falls
  back to free text; null-ish strings in optional price fields coerce to `None`.
  (#1038, #1051, #1057)
 ### Removed
 - The no-op `analyst_concurrency_limit` config knob; parallel analyst execution
  is planned for a later release. (#979)
 - The unused committed `uv.lock`. (#1030)
 ### Contributors
 Thanks to everyone who shaped this release through code, design, and reports:
 [@CadeYu](https://github.com/CadeYu), [@Zavianx](https://github.com/Zavianx), [@weijianz-opc](https://github.com/weijianz-opc), [@naltun](https://github.com/naltun), [@brahmasky](https://github.com/brahmasky), [@nik2208](https://github.com/nik2208), [@thieucong98](https://github.com/thieucong98), [@Derekko-web](https://github.com/Derekko-web), [@LukiPrince](https://github.com/LukiPrince), [@Eddieargenal](https://github.com/Eddieargenal), [@Ghraven](https://github.com/Ghraven), [@ms32035](https://github.com/ms32035), [@yting27](https://github.com/yting27), [@nyxst4ck](https://github.com/nyxst4ck), [@KenCheung-AIxFinance](https://github.com/KenCheung-AIxFinance), [@yangyusheng2n](https://github.com/yangyusheng2n), [@fareloj](https://github.com/fareloj), [@haosenwang1018](https://github.com/haosenwang1018), [@octo-patch](https://github.com/octo-patch), [@seifenk](https://github.com/seifenk), [@CaoYuhaoCarl](https://github.com/CaoYuhaoCarl), [@mihailnica10](https://github.com/mihailnica10), [@Dado-hash](https://github.com/Dado-hash), [@Handsomemikezzz](https://github.com/Handsomemikezzz), [@ydhawesome](https://github.com/ydhawesome), [@macd2](https://github.com/macd2), [@AyushKar2005](https://github.com/AyushKar2005), [@wildhuman](https://github.com/wildhuman), [@robert23kim](https://github.com/robert23kim), [@bngness](https://github.com/bngness), [@tedix-rodrigo](https://github.com/tedix-rodrigo), [@malaccan](https://github.com/malaccan), [@rfalken78](https://github.com/rfalken78), [@dengli1971-droid](https://github.com/dengli1971-droid), [@proofconcept39](https://github.com/proofconcept39), [@prasta1](https://github.com/prasta1), [@liximin](https://github.com/liximin), [@jeffhuen](https://github.com/jeffhuen), [@mazar](https://github.com/mazar), [@soyangelromero](https://github.com/soyangelromero), [@CNQQC](https://github.com/CNQQC), [@dovetaill](https://github.com/dovetaill), [@fperdigon](https://github.com/fperdigon), [@gyx09212214-prog](https://github.com/gyx09212214-prog), [@RSXLX](https://github.com/RSXLX).
 ## [0.2.5] — 2026-05-11
 ### Added
@@ -28,7 +28,9 @@
 # TradingAgents: Multi-Agents LLM Financial Trading Framework
 ## News
- [2026-05] **TradingAgents v0.2.5** released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, `TRADINGAGENTS_*` env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening. See [CHANGELOG.md](CHANGELOG.md) for the full list.
+- [2026-07] **TradingAgents v0.3.1** released with correctness and stability fixes: Alpha Vantage look-ahead filtering, graph-router crash-safety, graph-shape-aware checkpoint resume, working crypto sentiment sources, a configurable LLM retry budget, Bedrock API-key auth, and Claude Sonnet 5 / Fable 5 support. See [CHANGELOG.md](CHANGELOG.md) for the full list.
 - [2026-06] **TradingAgents v0.3.0** released with a verified data-access contract, an expanded provider registry (NVIDIA, Kimi, Groq, Mistral, Bedrock, and any OpenAI-compatible endpoint), FRED and Polymarket data vendors, a current-generation model catalog, and a CI gate.
 - [2026-05] **TradingAgents v0.2.5** released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, `TRADINGAGENTS_*` env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening.
 - [2026-04] **TradingAgents v0.2.4** released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix.
 - [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
 - [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
@@ -0,0 +1,52 @@
 # TradingAgents: Yahoo Finance SSH Proxy + Xiaomi MiMo LLM Support
 ## 概述
 本 patch 解决两个核心问题：
 1. **Yahoo Finance 云服务器 IP 被限流** — 通过国外服务器 SSH 代理中转请求
 2. **LLM 兼容性** — 支持小米 MiMo 等 OpenAI 兼容接口，增加 JSON 解析重试
 ## 修改文件
 | 文件 | 修改内容 |
 |------|----------|
 | `tradingagents/dataflows/yahoo_proxy.py` | **新增** — SSH 代理工具模块 |
 | `tradingagents/dataflows/stockstats_utils.py` | Yahoo Finance 代理降级 + `_yf_download_via_proxy` |
 | `tradingagents/dataflows/y_finance.py` | `get_YFin_data_online` 代理降级 |
 | `tradingagents/llm_clients/openai_client.py` | LLM JSON 解析重试机制 |
 ## 应用方法
 ```bash
 cd TradingAgents
 git apply patches/yahoo-proxy-xiaomi-support.patch
 ```
 ## 前置条件
 1. 国外服务器 SSH 凭据配置在 `secrets/credentials.env.enc`（变量 `FOREIGN_HOST`, `FOREIGN_PORT`, `FOREIGN_PASS`）
 2. `sshpass` 已安装：`apt install sshpass`
 3. Alpha Vantage API key（可选，用于基本面数据）
 ## 使用小米 MiMo 模型
 ```python
 import os
 os.environ["OPENAI_API_KEY"] = "your-xiaomi-api-key"
 os.environ["OPENAI_API_BASE"] = "https://token-plan-cn.xiaomimimo.com/v1"
 config = DEFAULT_CONFIG.copy()
 config["llm_provider"] = "openai"
 config["deep_think_llm"] = "mimo-v2.5-pro"
 config["quick_think_llm"] = "mimo-v2.5"
 ```
 ## 测试结果
 - ✅ Yahoo Finance SSH 代理：1137 行 OHLCV 数据获取成功
 - ✅ 小米 MiMo Pro：完整分析流程通过（技术/基本面/情绪/新闻/辩论/决策）
 - ✅ LLM JSON 重试：偶发解析错误自动恢复
 - ✅ Alpha Vantage：基本面数据正常
 ---
 *Generated: 2026-07-07 | Author: 小钰*
@@ -0,0 +1,419 @@
 diff --git a/tradingagents/dataflows/stockstats_utils.py b/tradingagents/dataflows/stockstats_utils.py
 index db880b0..cb97dc1 100644
 --- a/tradingagents/dataflows/stockstats_utils.py
 +++ b/tradingagents/dataflows/stockstats_utils.py
@@ -1,6 +1,8 @@
 import logging
 import os
 import time
 +import json
 +import subprocess
 from typing import Annotated
 import pandas as pd
@@ -19,6 +21,82 @@ logger = logging.getLogger(__name__)
 # enough to catch the year-old frames yfinance occasionally returns (#1021).
 MAX_OHLCV_STALE_DAYS = 10
 +_ssh_config_cache = None
 +
 +def _get_ssh_config():
 +    """Load SSH credentials for foreign server from vault (cached)."""
 +    global _ssh_config_cache
 +    if _ssh_config_cache is not None:
 +        return _ssh_config_cache
 +    result = subprocess.run(
 +        ["bash", "-c",
 +         'source <(bash /root/.openclaw/workspace/scripts/vault.sh decrypt '
 +         '/root/.openclaw/workspace/secrets/credentials.env.enc) '
 +         '&& echo "$FOREIGN_HOST $FOREIGN_PORT $FOREIGN_PASS"'],
 +        capture_output=True, text=True, timeout=10
 +    )
 +    parts = result.stdout.strip().split()
 +    if len(parts) != 3:
 +        raise RuntimeError(f"Failed to load SSH config: {result.stderr}")
 +    _ssh_config_cache = (parts[0], int(parts[1]), parts[2])
 +    return _ssh_config_cache
 +
 +
 +def _fetch_via_ssh(url: str, timeout: int = 30) -> str:
 +    """Fetch a URL through the foreign server via SSH tunnel."""
 +    host, port, password = _get_ssh_config()
 +    cmd = [
 +        "sshpass", "-p", password,
 +        "ssh", "-o", "StrictHostKeyChecking=no",
 +        "-o", "ConnectTimeout=15",
 +        "-p", str(port), f"root@{host}",
 +        f'curl -s "{url}" -H "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"'
 +    ]
 +    result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
 +    if result.returncode != 0:
 +        raise RuntimeError(f"SSH fetch failed: {result.stderr}")
 +    return result.stdout
 +
 +
 +def _yf_download_via_proxy(symbol: str, start: str, end: str) -> pd.DataFrame:
 +    """Download OHLCV data via SSH proxy when local Yahoo Finance is rate-limited."""
 +    import datetime
 +    # Convert dates to timestamps
 +    start_ts = int(pd.Timestamp(start).timestamp())
 +    end_ts = int(pd.Timestamp(end).timestamp())
 +    
 +    url = (f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}"
 +           f"?period1={start_ts}&period2={end_ts}&interval=1d")
 +    
 +    data_str = _fetch_via_ssh(url)
 +    parsed = json.loads(data_str)
 +    
 +    result = parsed.get("chart", {}).get("result", [])
 +    if not result:
 +        return pd.DataFrame()
 +    
 +    ts = result[0].get("timestamp", [])
 +    indicators = result[0].get("indicators", {}).get("quote", [{}])[0]
 +    
 +    if not ts:
 +        return pd.DataFrame()
 +    
 +    df = pd.DataFrame({
 +        "Date": pd.to_datetime(ts, unit="s"),
 +        "Open": indicators.get("open", []),
 +        "High": indicators.get("high", []),
 +        "Low": indicators.get("low", []),
 +        "Close": indicators.get("close", []),
 +        "Volume": indicators.get("volume", []),
 +    })
 +    
 +    # Convert None values to NaN
 +    for col in ["Open", "High", "Low", "Close", "Volume"]:
 +        df[col] = pd.to_numeric(df[col], errors="coerce")
 +    
 +    df = df.dropna(subset=["Close"])
 +    return df
 +
 def yf_retry(func, max_retries=3, base_delay=2.0):
     """Execute a yfinance call with exponential backoff on rate limits.
@@ -36,6 +114,7 @@ def yf_retry(func, max_retries=3, base_delay=2.0):
                 logger.warning(f"Yahoo Finance rate limited, retrying in {delay:.0f}s (attempt {attempt + 1}/{max_retries})")
                 time.sleep(delay)
             else:
 +                logger.warning("Yahoo Finance local exhausted, will let caller handle fallback")
                 raise
@@ -122,6 +201,82 @@ def _assert_ohlcv_not_stale(
         )
 +def _load_ohlcv_alpha_vantage(symbol: str, curr_date: str) -> pd.DataFrame:
 +    """Fetch OHLCV data from Alpha Vantage, filtered to prevent look-ahead bias."""
 +    from .alpha_vantage_common import _make_api_request
 +    from io import StringIO
 +
 +    canonical = normalize_symbol(symbol)
 +    curr_date_dt = pd.to_datetime(curr_date)
 +    today_date = pd.Timestamp.today()
 +    start_date = today_date - pd.DateOffset(years=5)
 +    start_str = start_date.strftime("%Y-%m-%d")
 +
 +    # Determine outputsize based on how far back we need
 +    days_from_today = (today_date - start_date).days
 +    outputsize = "compact" if days_from_today < 100 else "full"
 +
 +    params = {
 +        "symbol": canonical,
 +        "outputsize": outputsize,
 +        "datatype": "csv",
 +    }
 +
 +    try:
 +        csv_text = _make_api_request("TIME_SERIES_DAILY", params)
 +    except Exception as e:
 +        raise NoMarketDataError(symbol, canonical, f"Alpha Vantage error: {e}") from e
 +
 +    if not csv_text or not csv_text.strip():
 +        raise NoMarketDataError(symbol, canonical, "Alpha Vantage returned empty data")
 +
 +    try:
 +        df = pd.read_csv(StringIO(csv_text))
 +    except Exception as e:
 +        raise NoMarketDataError(symbol, canonical, f"Failed to parse Alpha Vantage CSV: {e}") from e
 +
 +    if df.empty:
 +        raise NoMarketDataError(symbol, canonical, "Alpha Vantage returned no rows")
 +
 +    # Alpha Vantage CSV has 'timestamp' column; normalize to 'Date'
 +    date_col = df.columns[0]  # usually 'timestamp'
 +    df = df.rename(columns={date_col: "Date"})
 +
 +    # Keep only standard OHLCV columns
 +    keep_cols = ["Date"]
 +    col_map = {}
 +    for av_col, std_col in [
 +        ("open", "Open"), ("high", "High"), ("low", "Low"),
 +        ("close", "Close"), ("adjusted_close", "Close"), ("volume", "Volume"),
 +    ]:
 +        for c in df.columns:
 +            if c.lower().replace(" ", "_") == av_col:
 +                if std_col not in keep_cols:
 +                    keep_cols.append(std_col)
 +                    col_map[c] = std_col
 +                break
 +    df = df.rename(columns=col_map)
 +    # Prefer adjusted_close over close for "Close"
 +    if "adjusted_close" in [c.lower().replace(" ", "_") for c in df.columns]:
 +        for c in df.columns:
 +            if c.lower().replace(" ", "_") == "adjusted_close":
 +                df["Close"] = pd.to_numeric(df[c], errors="coerce")
 +                break
 +
 +    available = [c for c in ["Date", "Open", "High", "Low", "Close", "Volume"] if c in df.columns]
 +    df = df[available]
 +
 +    # Filter to curr_date
 +    df["Date"] = pd.to_datetime(df["Date"], errors="coerce")
 +    df = df.dropna(subset=["Date"])
 +    df = df[df["Date"] <= curr_date_dt].sort_values("Date")
 +
 +    if df.empty:
 +        raise NoMarketDataError(symbol, canonical, "No data on or before " + curr_date)
 +
 +    return df
 +
 +
 def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
     """Fetch OHLCV data with caching, filtered to prevent look-ahead bias.
@@ -138,24 +293,24 @@ def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
     config = get_config()
     curr_date_dt = pd.to_datetime(curr_date)
 +    # Check which vendor to use for OHLCV
 +    vendor_config = config.get("data_vendors", {}).get("core_stock_apis", "yfinance")
 +    use_alpha_vantage = "alpha_vantage" in vendor_config
 +
     # Cache uses a fixed window (5y to today) so one file per symbol.
     today_date = pd.Timestamp.today()
     start_date = today_date - pd.DateOffset(years=5)
     start_str = start_date.strftime("%Y-%m-%d")
 -    # yfinance ``end`` is EXCLUSIVE; request tomorrow so today's row is included
 -    # when curr_date is the current day (#986). Look-ahead is still prevented by
 -    # the curr_date filter below.
     end_str = (today_date + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
 +    cache_prefix = "AV" if use_alpha_vantage else "YFin"
     os.makedirs(config["data_cache_dir"], exist_ok=True)
     data_file = os.path.join(
         config["data_cache_dir"],
 -        f"{safe_symbol}-YFin-data-{start_str}-{end_str}.csv",
 +        f"{safe_symbol}-{cache_prefix}-data-{start_str}-{end_str}.csv",
     )
 -    # A cached file may be empty if a prior fetch failed (unknown symbol,
 -    # transient rate limit). Treat an empty/columnless cache as a miss and
 -    # re-fetch rather than serving the poisoned file forever.
 +    # A cached file may be empty if a prior fetch failed. Treat empty cache as miss.
     data = None
     if os.path.exists(data_file):
         cached = pd.read_csv(data_file, on_bad_lines="skip", encoding="utf-8")
@@ -163,30 +318,53 @@ def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
             data = cached
     if data is None:
 -        downloaded = yf_retry(lambda: yf.download(
 -            canonical,
 -            start=start_str,
 -            end=end_str,
 -            multi_level_index=False,
 -            progress=False,
 -            auto_adjust=True,
 -        ))
 -        downloaded = _ensure_date_column(downloaded.reset_index())
 -        # Only cache real data — never persist an empty frame.
 -        if downloaded.empty or "Close" not in downloaded.columns:
 -            raise NoMarketDataError(
 -                symbol, canonical, "Yahoo Finance returned no rows"
 -            )
 +        if use_alpha_vantage:
 +            downloaded = _load_ohlcv_alpha_vantage(symbol, curr_date)
 +        else:
 +            downloaded = None
 +            try:
 +                raw = yf_retry(lambda: yf.download(
 +                    canonical,
 +                    start=start_str,
 +                    end=end_str,
 +                    multi_level_index=False,
 +                    progress=False,
 +                    auto_adjust=True,
 +                ))
 +                downloaded = _ensure_date_column(raw.reset_index())
 +            except Exception as yf_err:
 +                pass  # Will try proxy below
 +
 +            # If local yfinance failed or returned empty, try SSH proxy
 +            if downloaded is None or downloaded.empty or "Close" not in downloaded.columns:
 +                if downloaded is None or downloaded.empty:
 +                    logger.warning(f"Yahoo Finance local failed, trying SSH proxy...")
 +                try:
 +                    downloaded = _yf_download_via_proxy(canonical, start_str, end_str)
 +                except Exception as proxy_err:
 +                    raise NoMarketDataError(
 +                        symbol, canonical, f"Yahoo Finance proxy also failed: {proxy_err}"
 +                    ) from proxy_err
 +                if downloaded.empty:
 +                    raise NoMarketDataError(
 +                        symbol, canonical, f"Yahoo Finance proxy also returned no data"
 +                    )
 +            if downloaded.empty or "Close" not in downloaded.columns:
 +                raise NoMarketDataError(
 +                    symbol, canonical, "Yahoo Finance returned no rows"
 +                )
 +            downloaded = _clean_dataframe(downloaded)
 +
         downloaded.to_csv(data_file, index=False, encoding="utf-8")
         data = downloaded
 -    data = _clean_dataframe(data)
 +    if not use_alpha_vantage:
 +        data = _clean_dataframe(data)
     # Filter to curr_date to prevent look-ahead bias in backtesting
     data = data[data["Date"] <= curr_date_dt]
 -    # Reject a stale frame (latest row far older than curr_date) rather than
 -    # feeding year-old prices into indicators (#1021).
 +    # Reject a stale frame
     _assert_ohlcv_not_stale(data, curr_date, symbol, canonical)
     return data
 diff --git a/tradingagents/dataflows/y_finance.py b/tradingagents/dataflows/y_finance.py
 index fdb49d5..9e36c6d 100644
 --- a/tradingagents/dataflows/y_finance.py
 +++ b/tradingagents/dataflows/y_finance.py
@@ -1,13 +1,17 @@
 +import logging
 from datetime import datetime
 from typing import Annotated
 import pandas as pd
 import yfinance as yf
 +
 +logger = logging.getLogger(__name__)
 from dateutil.relativedelta import relativedelta
 from .stockstats_utils import (
     StockstatsUtils,
     _assert_ohlcv_not_stale,
 +    _yf_download_via_proxy,
     filter_financials_by_date,
     load_ohlcv,
     yf_retry,
@@ -32,7 +36,19 @@ def get_YFin_data_online(
     # end_date row (and the current day when end_date is today). Request one day
     # past end_date so the requested range is actually inclusive (#986/#987).
     end_inclusive = (end_dt + relativedelta(days=1)).strftime("%Y-%m-%d")
 -    data = yf_retry(lambda: ticker.history(start=start_date, end=end_inclusive))
 +    try:
 +        data = yf_retry(lambda: ticker.history(start=start_date, end=end_inclusive))
 +    except Exception:
 +        # Fallback: use SSH proxy via foreign server
 +        logger.warning(f"Yahoo Finance local failed for {symbol}, trying SSH proxy...")
 +        proxy_df = _yf_download_via_proxy(canonical, start_date, end_inclusive)
 +        if proxy_df.empty:
 +            raise NoMarketDataError(
 +                symbol, canonical, f"no rows between {start_date} and {end_date} (proxy also failed)"
 +            )
 +        # Convert to match expected format
 +        proxy_df = proxy_df.set_index("Date")
 +        data = proxy_df
     # Empty result means the symbol is unknown/delisted. Raise a typed error
     # instead of returning prose: the routing layer turns it into a single
 diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py
 index bf6e554..717750d 100644
 --- a/tradingagents/llm_clients/openai_client.py
 +++ b/tradingagents/llm_clients/openai_client.py
@@ -33,7 +33,21 @@ class NormalizedChatOpenAI(ChatOpenAI):
     """
     def invoke(self, input, config=None, **kwargs):
 -        return normalize_content(super().invoke(input, config, **kwargs))
 +        import json as _json
 +        import time as _time
 +        import logging as _logging
 +        _logger = _logging.getLogger(__name__)
 +        max_retries = 3
 +        for attempt in range(max_retries):
 +            try:
 +                return normalize_content(super().invoke(input, config, **kwargs))
 +            except (_json.JSONDecodeError, Exception) as e:
 +                if 'JSONDecodeError' in type(e).__name__ or 'Expecting value' in str(e):
 +                    if attempt < max_retries - 1:
 +                        _logger.warning(f"LLM returned invalid JSON, retrying ({attempt+1}/{max_retries}): {e}")
 +                        _time.sleep(2 * (attempt + 1))
 +                        continue
 +                raise
     def with_structured_output(self, schema, *, method=None, **kwargs):
         caps = get_capabilities(self.model_name)
 diff --git a/tradingagents/dataflows/yahoo_proxy.py b/tradingagents/dataflows/yahoo_proxy.py
 new file mode 100644
 index 0000000..c06e0ad
 --- /dev/null
 +++ b/tradingagents/dataflows/yahoo_proxy.py
@@ -0,0 +1,53 @@
 +#!/usr/bin/env python3
 +"""Yahoo Finance proxy via foreign server.
 +
 +Routes yfinance requests through the foreign server to bypass local IP rate limiting.
 +"""
 +import subprocess
 +import json
 +import os
 +import sys
 +
 +def _get_ssh_config():
 +    """Load SSH credentials from vault."""
 +    result = subprocess.run(
 +        ["bash", "-c", 'source <(bash /root/.openclaw/workspace/scripts/vault.sh decrypt /root/.openclaw/workspace/secrets/credentials.env.enc) && echo "$FOREIGN_HOST $FOREIGN_PORT $FOREIGN_PASS"'],
 +        capture_output=True, text=True, timeout=10
 +    )
 +    parts = result.stdout.strip().split()
 +    if len(parts) != 3:
 +        raise RuntimeError(f"Failed to load SSH config: {result.stderr}")
 +    return parts[0], int(parts[1]), parts[2]
 +
 +def fetch_yahoo_via_ssh(url: str, timeout: int = 30) -> str:
 +    """Fetch a Yahoo Finance URL through the foreign server via SSH."""
 +    host, port, password = _get_ssh_config()
 +    cmd = [
 +        "sshpass", "-p", password,
 +        "ssh", "-o", "StrictHostKeyChecking=no",
 +        "-o", f"ConnectTimeout=15",
 +        "-p", str(port), f"root@{host}",
 +        f'curl -s "{url}" -H "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"'
 +    ]
 +    result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
 +    if result.returncode != 0:
 +        raise RuntimeError(f"SSH fetch failed: {result.stderr}")
 +    return result.stdout
 +
 +def test_connection() -> bool:
 +    """Test if Yahoo Finance is accessible via foreign server."""
 +    try:
 +        data = fetch_yahoo_via_ssh(
 +            "https://query2.finance.yahoo.com/v8/finance/chart/AAPL?interval=1d&range=5d"
 +        )
 +        parsed = json.loads(data)
 +        return "chart" in parsed and "result" in parsed.get("chart", {})
 +    except Exception as e:
 +        print(f"Connection test failed: {e}", file=sys.stderr)
 +        return False
 +
 +if __name__ == "__main__":
 +    if test_connection():
 +        print("✅ Yahoo Finance proxy via foreign server: OK")
 +    else:
 +        print("❌ Yahoo Finance proxy via foreign server: FAILED")
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "tradingagents"
-version = "0.2.5"
+version = "0.3.1"
 description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -0,0 +1,66 @@
 """Quick smoke test for TradingAgents deployment.
 Uses OpenRouter + free Gemini model to minimize cost.
 """
 import os
 import json
 # Load OpenRouter API key from openclaw config
 with open(os.path.expanduser("~/.openclaw/openclaw.json")) as f:
    oc_config = json.load(f)
 openrouter_key = oc_config.get("env", {}).get("OPENROUTER_API_KEY", "")
 if not openrouter_key:
    raise RuntimeError("OPENROUTER_API_KEY not found in openclaw.json")
 os.environ["OPENROUTER_API_KEY"] = openrouter_key
 os.environ["ALPHA_VANTAGE_API_KEY"] = "W5PTA2J5OB16W02L"
 os.environ["OPENAI_API_KEY"] = "tp-ce6eaqg1gh1uev46i4oexh4sahloyfo2hfpxuc2n9m7yl9dd"
 os.environ["OPENAI_API_BASE"] = "https://token-plan-cn.xiaomimimo.com/v1"
 # Import after env var is set
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
 # Configure: OpenRouter + Gemini (free/cheap model)
 config = DEFAULT_CONFIG.copy()
 config["llm_provider"] = "openai"
 config["deep_think_llm"] = "mimo-v2.5-pro"
 config["quick_think_llm"] = "mimo-v2.5"
 config["llm_max_retries"] = 6
 config["temperature"] = 0.1  # Lower temperature for more stable output
 config["data_vendors"] = {
    "core_stock_apis": "yfinance",
    "technical_indicators": "yfinance",
    "fundamental_data": "alpha_vantage",
    "news_data": "yfinance",
    "macro_data": "fred",
    "prediction_markets": "polymarket",
 }
 config["max_debate_rounds"] = 1
 config["max_risk_discuss_rounds"] = 1
 config["output_language"] = "Chinese"
 print("=" * 60)
 print("TradingAgents Smoke Test")
 print(f"Provider: {config['llm_provider']}")
 print(f"Deep Model: {config['deep_think_llm']}")
 print(f"Quick Model: {config['quick_think_llm']}")
 print("=" * 60)
 # Test with a simple A-share ticker
 ta = TradingAgentsGraph(debug=True, config=config)
 print("\nRunning analysis for 600519.SS (Kweichow Moutai) on 2026-01-15...")
 print("(This will take several minutes due to multiple LLM calls)\n")
 try:
    _, decision = ta.propagate("AAPL", "2026-01-15")
    print("\n" + "=" * 60)
    print("DECISION RESULT:")
    print("=" * 60)
    print(decision)
    print("\n✅ Smoke test PASSED!")
 except Exception as e:
    print(f"\n❌ Smoke test FAILED: {e}")
    import traceback
    traceback.print_exc()
@@ -0,0 +1,43 @@
 """使用小米 mimo-v2.5-pro 模型运行 TradingAgents 示例"""
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
 config = DEFAULT_CONFIG.copy()
 # 使用 openai_compatible 对接小米 API
 config["llm_provider"] = "openai_compatible"
 config["backend_url"] = "https://token-plan-cn.xiaomimimo.com/v1"
 config["deep_think_llm"] = "mimo-v2.5-pro"
 config["quick_think_llm"] = "mimo-v2.5-pro"
 # 配置数据源：Yahoo Finance优先，Alpha Vantage作为备选
 config['data_vendors'] = {
    'core_stock_apis': 'yfinance,alpha_vantage',
    'technical_indicators': 'yfinance,alpha_vantage',
    'fundamental_data': 'yfinance,alpha_vantage',
    'news_data': 'yfinance,alpha_vantage',
    'macro_data': 'fred',
    'prediction_markets': 'polymarket',
 }
 # 设置 API Key（也可通过环境变量 OPENAI_COMPATIBLE_API_KEY）
 import os
 os.environ["OPENAI_COMPATIBLE_API_KEY"] = "tp-ce6eaqg1gh1uev46i4oexh4sahloyfo2hfpxuc2n9m7yl9dd"
 os.environ["FRED_API_KEY"] = "825283926dd016ea66e96cc919e047da"
 os.environ["ALPHA_VANTAGE_API_KEY"] = "W5PTA2J5OB16W02L"
 # 减少辩论轮数加速测试
 config["max_debate_rounds"] = 1
 config["max_risk_discuss_rounds"] = 1
 print("配置完成，开始运行 TradingAgents...")
 print(f"Provider: {config['llm_provider']}")
 print(f"Backend: {config['backend_url']}")
 print(f"Model: {config['deep_think_llm']}")
 ta = TradingAgentsGraph(debug=True, config=config)
 # 测试分析茅台（A股）
 _, decision = ta.propagate("600519.SS", "2026-01-15")
 print("\n=== 交易决策 ===")
 print(decision)
@@ -1,11 +1,16 @@
 """Alpha Vantage request hardening.
-Regressions for #990 (no request timeout -> can hang) and #991 (invalid-key
+Regressions for #990 (no request timeout -> can hang), #991 (invalid-key
-responses mislabeled as rate limits and silently treated as transient).
+responses mislabeled as rate limits and silently treated as transient), and
 #1115 (fundamentals look-ahead filter never ran because the payload is a JSON
 string, not a dict).
 """
 import json
 import pytest
 import tradingagents.dataflows.alpha_vantage_common as av
 import tradingagents.dataflows.alpha_vantage_fundamentals as avf
 class _FakeResponse:
@@ -52,3 +57,40 @@ def test_invalid_key_not_mislabeled_as_rate_limit(monkeypatch):
    with pytest.raises(av.AlphaVantageRateLimitError):  # sanity: rate-limit path still distinct
        monkeypatch.setattr(av.requests, "get", _patched_get('{"Note": "API call frequency is 5 calls per minute."}'))
        av._make_api_request("TIME_SERIES_DAILY", {"symbol": "AAPL"})
 _FUNDAMENTALS_JSON = json.dumps({
    "symbol": "AAPL",
    "annualReports": [
        {"fiscalDateEnding": "2025-12-31", "totalAssets": "1"},   # future -> must drop
        {"fiscalDateEnding": "2023-12-31", "totalAssets": "2"},   # past   -> must keep
    ],
    "quarterlyReports": [
        {"fiscalDateEnding": "2024-06-30", "totalAssets": "3"},   # future -> must drop
        {"fiscalDateEnding": "2023-09-30", "totalAssets": "4"},   # past   -> must keep
    ],
 })
@pytest.mark.unit
 def test_fundamentals_look_ahead_filter_runs_on_json_string(monkeypatch):
    # #1115: the payload arrives as a JSON *string*; the old dict-only guard let
    # future-dated fiscal periods leak into historical runs.
    monkeypatch.setattr(avf, "_make_api_request", lambda fn, params: _FUNDAMENTALS_JSON)
    out = avf.get_balance_sheet("AAPL", curr_date="2024-01-01")
    assert isinstance(out, str)  # callers still receive a str
    parsed = json.loads(out)
    assert [r["fiscalDateEnding"] for r in parsed["annualReports"]] == ["2023-12-31"]
    assert [r["fiscalDateEnding"] for r in parsed["quarterlyReports"]] == ["2023-09-30"]
@pytest.mark.unit
 def test_fundamentals_no_curr_date_passes_through(monkeypatch):
    monkeypatch.setattr(avf, "_make_api_request", lambda fn, params: _FUNDAMENTALS_JSON)
    assert avf.get_income_statement("AAPL") == _FUNDAMENTALS_JSON
@pytest.mark.unit
 def test_fundamentals_non_json_body_unchanged(monkeypatch):
    monkeypatch.setattr(avf, "_make_api_request", lambda fn, params: "not-json")
    assert avf.get_cashflow("AAPL", curr_date="2024-01-01") == "not-json"
@@ -57,6 +57,16 @@ class TestEffortGate:
        mod.AnthropicClient(model=model, effort="low", api_key="x").get_llm()
        assert captured["kwargs"]["effort"] == "low"
    @pytest.mark.parametrize(
        "model",
        # Claude 5 family uses single-number version IDs; all are effort-capable.
        ["claude-sonnet-5", "claude-fable-5", "claude-mythos-5"],
    )
    def test_claude_5_family_receives_effort(self, monkeypatch, model):
        captured = _capture_kwargs(monkeypatch)
        mod.AnthropicClient(model=model, effort="high", api_key="x").get_llm()
        assert captured["kwargs"]["effort"] == "high"
    def test_mythos_preview_receives_effort(self, monkeypatch):
        captured = _capture_kwargs(monkeypatch)
        mod.AnthropicClient(
@@ -35,12 +35,46 @@ def test_helpful_error_when_langchain_aws_absent(monkeypatch):
        create_llm_client("bedrock", "m").get_llm()
 def _capture_kwargs(monkeypatch):
    """Stub _bedrock_class so the constructor kwargs are testable without the
    optional langchain-aws extra installed."""
    import tradingagents.llm_clients.bedrock_client as bc
    captured = {}
    class _FakeChat:
        def __init__(self, **kwargs):
            captured.update(kwargs)
    monkeypatch.setattr(bc, "_bedrock_class", lambda: _FakeChat)
    return captured
@pytest.mark.unit
 def test_bearer_token_passed_as_api_key(monkeypatch):
    # #1103: a Bedrock API key authenticates without AWS access keys.
    captured = _capture_kwargs(monkeypatch)
    monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "bt-secret")
    monkeypatch.setenv("AWS_DEFAULT_REGION", "us-east-1")
    create_llm_client("bedrock", "us.anthropic.claude-opus-4-8-v1:0").get_llm()
    assert captured["api_key"] == "bt-secret"
    assert captured["region_name"] == "us-east-1"
@pytest.mark.unit
 def test_no_bearer_token_omits_api_key(monkeypatch):
    # Without a token, fall back to the AWS credential chain (no api_key kwarg).
    captured = _capture_kwargs(monkeypatch)
    monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False)
    create_llm_client("bedrock", "us.anthropic.claude-opus-4-8-v1:0").get_llm()
    assert "api_key" not in captured
@pytest.mark.unit
 def test_construction_when_extra_installed(monkeypatch):
    pytest.importorskip("langchain_aws")
    import tradingagents.llm_clients.bedrock_client as bc
    monkeypatch.setattr(bc, "_BEDROCK_CLASS", None)
    monkeypatch.setenv("AWS_DEFAULT_REGION", "eu-west-1")
-    llm = create_llm_client("bedrock", "us.anthropic.claude-sonnet-4-6-v1:0").get_llm()
+    llm = create_llm_client("bedrock", "us.anthropic.claude-sonnet-5").get_llm()
    assert type(llm).__name__ == "NormalizedChatBedrockConverse"
    assert llm.region_name == "eu-west-1"
@@ -140,5 +140,79 @@ class TestCheckpointResume(unittest.TestCase):
        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
 class TestCheckpointSignature(unittest.TestCase):
    """A different graph shape (analyst selection / depth / asset mode) must not
    resume the previous run's checkpoint (#1089)."""
    def setUp(self):
        self.tmpdir = tempfile.mkdtemp()
        self.ticker = "TEST"
        self.date = "2026-04-20"
    def test_empty_signature_is_legacy_id(self):
        self.assertEqual(
            thread_id(self.ticker, self.date),
            thread_id(self.ticker, self.date, ""),
        )
    def test_signature_changes_thread_id(self):
        legacy = thread_id(self.ticker, self.date)
        sig_a = thread_id(self.ticker, self.date, "analysts=market,news|asset=stock")
        sig_b = thread_id(self.ticker, self.date, "analysts=market|asset=stock")
        self.assertNotEqual(sig_a, sig_b)          # different graph shapes differ
        self.assertNotEqual(legacy, sig_a)         # signature-keyed differs from legacy
        self.assertEqual(                          # same inputs are stable
            sig_a, thread_id(self.ticker, self.date, "analysts=market,news|asset=stock")
        )
    def test_different_signature_starts_fresh(self):
        global _should_crash
        builder = _build_graph()
        sig1 = "analysts=market,news,fundamentals|asset=stock"
        sig2 = "analysts=market|asset=stock"       # dropped analysts -> different graph
        _should_crash = True
        tid1 = thread_id(self.ticker, self.date, sig1)
        with get_checkpointer(self.tmpdir, self.ticker) as saver:
            graph = builder.compile(checkpointer=saver)
            with self.assertRaises(RuntimeError):
                graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid1}})
        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date, sig1))
        # A different graph shape has no checkpoint to resume from.
        self.assertFalse(has_checkpoint(self.tmpdir, self.ticker, self.date, sig2))
        _should_crash = False
        tid2 = thread_id(self.ticker, self.date, sig2)
        self.assertNotEqual(tid1, tid2)
        with get_checkpointer(self.tmpdir, self.ticker) as saver:
            graph = builder.compile(checkpointer=saver)
            result = graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid2}})
        self.assertEqual(result["count"], 11)
        # sig1's checkpoint remains untouched.
        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date, sig1))
    def test_run_signature_captures_graph_shape(self):
        from tradingagents.graph.trading_graph import TradingAgentsGraph
        # Build a bare instance to exercise the pure helper without heavy __init__.
        g = object.__new__(TradingAgentsGraph)
        g.selected_analysts = ("market", "news")
        g.config = {"max_debate_rounds": 1, "max_risk_discuss_rounds": 1}
        base = g._run_signature("stock")
        self.assertNotEqual(base, g._run_signature("crypto"))     # asset mode
        g.selected_analysts = ("market",)
        self.assertNotEqual(base, g._run_signature("stock"))      # analyst selection
        g.selected_analysts = ("market", "news")
        g.config = {"max_debate_rounds": 3, "max_risk_discuss_rounds": 1}
        self.assertNotEqual(base, g._run_signature("stock"))      # debate depth
        g.config = {"max_debate_rounds": 1, "max_risk_discuss_rounds": 5}
        self.assertNotEqual(base, g._run_signature("stock"))      # risk depth
        # Stable for identical inputs.
        g.config = {"max_debate_rounds": 1, "max_risk_discuss_rounds": 1}
        self.assertEqual(base, g._run_signature("stock"))
 if __name__ == "__main__":
    unittest.main()
@@ -0,0 +1,100 @@
 """Configurable LLM SDK retry budget (#1090/#1091).
 A single transient 429 burst used to kill an otherwise-healthy multi-agent run
 because each provider SDK's max_retries (default 2) was not exposed. This adds an
 opt-in llm_max_retries knob forwarded to every provider chat client.
 """
 from __future__ import annotations
 import importlib
 import pytest
 import tradingagents.default_config as default_config_module
 from tradingagents.graph.trading_graph import TradingAgentsGraph, _coerce_max_retries
 # --- coercion / validation -------------------------------------------------
@pytest.mark.unit
@pytest.mark.parametrize("value,expected", [(0, 0), (2, 2), (10, 10), ("6", 6)])
 def test_coerce_accepts_non_negative_ints_and_numeric_strings(value, expected):
    assert _coerce_max_retries(value) == expected
@pytest.mark.unit
@pytest.mark.parametrize("bad", [-1, "-3"])
 def test_coerce_rejects_negative(bad):
    with pytest.raises(ValueError, match=">= 0"):
        _coerce_max_retries(bad)
@pytest.mark.unit
@pytest.mark.parametrize("bad", [True, False])
 def test_coerce_rejects_booleans(bad):
    with pytest.raises(ValueError, match="boolean"):
        _coerce_max_retries(bad)
@pytest.mark.unit
@pytest.mark.parametrize("bad", ["abc", "1.5", None])
 def test_coerce_rejects_non_integers(bad):
    with pytest.raises(ValueError, match="integer"):
        _coerce_max_retries(bad)
 # --- forwarding into provider kwargs --------------------------------------
 def _bare_graph(config):
    g = object.__new__(TradingAgentsGraph)
    g.config = config
    return g
@pytest.mark.unit
 def test_not_forwarded_when_unset():
    kwargs = _bare_graph({"llm_provider": "openai", "llm_max_retries": None})._get_provider_kwargs()
    assert "max_retries" not in kwargs
@pytest.mark.unit
@pytest.mark.parametrize("provider", ["openai", "anthropic", "google"])
 def test_forwarded_across_providers(provider):
    kwargs = _bare_graph({"llm_provider": provider, "llm_max_retries": 6})._get_provider_kwargs()
    assert kwargs["max_retries"] == 6
@pytest.mark.unit
 def test_forwarded_env_string_is_coerced():
    # env vars arrive as strings; the consumer coerces (like temperature)
    kwargs = _bare_graph({"llm_provider": "openai", "llm_max_retries": "4"})._get_provider_kwargs()
    assert kwargs["max_retries"] == 4
@pytest.mark.unit
 def test_invalid_config_value_fails_loudly():
    with pytest.raises(ValueError):
        _bare_graph({"llm_provider": "openai", "llm_max_retries": -1})._get_provider_kwargs()
 # --- env overlay -----------------------------------------------------------
 def _reload_with_env(monkeypatch, **overrides):
    for key in list(default_config_module._ENV_OVERRIDES):
        monkeypatch.delenv(key, raising=False)
    for key, val in overrides.items():
        monkeypatch.setenv(key, val)
    return importlib.reload(default_config_module)
@pytest.mark.unit
 def test_default_is_none(monkeypatch):
    dc = _reload_with_env(monkeypatch)
    assert dc.DEFAULT_CONFIG["llm_max_retries"] is None
@pytest.mark.unit
 def test_env_override_sets_config(monkeypatch):
    dc = _reload_with_env(monkeypatch, TRADINGAGENTS_LLM_MAX_RETRIES="8")
    # None-default key: env value arrives as a string and is coerced downstream.
    assert dc.DEFAULT_CONFIG["llm_max_retries"] == "8"
    assert _coerce_max_retries(dc.DEFAULT_CONFIG["llm_max_retries"]) == 8
@@ -0,0 +1,25 @@
 """Guard the news analyst prompt against tool-signature drift (#1116).
 The prompt used to advertise ``get_news(query, ...)`` while the tool takes a
 ``ticker``, tricking the LLM into hallucinating free-text query calls.
 """
 import inspect
 import pytest
 import tradingagents.agents.analysts.news_analyst as na
 from tradingagents.agents.utils.news_data_tools import get_news
@pytest.mark.unit
 def test_get_news_takes_ticker_not_query():
    arg_names = set(get_news.args.keys())
    assert "ticker" in arg_names
    assert "query" not in arg_names
@pytest.mark.unit
 def test_news_prompt_matches_get_news_signature():
    src = inspect.getsource(na)
    assert "get_news(ticker, start_date, end_date)" in src
    assert "get_news(query" not in src
@@ -190,3 +190,25 @@ class TestFormatterHandlesRssPosts:
        assert "1234↑" in out
        assert "56c" in out
        assert "via RSS" not in out
@pytest.mark.unit
 class TestCryptoSearchTerm:
    """A crypto pair (BTC-USD) barely matches Reddit text; search the base (#1113)."""
    def _captured_ticker(self, ticker):
        seen = {}
        def fake_fetch(t, sub, limit, timeout):
            seen["ticker"] = t
            return []
        with patch.object(reddit, "_fetch_subreddit", side_effect=fake_fetch):
            reddit.fetch_reddit_posts(ticker, subreddits=("stocks",), inter_request_delay=0)
        return seen["ticker"]
    def test_crypto_pair_searches_base(self):
        assert self._captured_ticker("BTC-USD") == "BTC"
    def test_equity_passes_through(self):
        assert self._captured_ticker("NVDA") == "NVDA"
@@ -0,0 +1,81 @@
 """Shared-router / path_map completeness (#1088).
 Both `should_continue_risk_analysis` (three risk edges) and
 `should_continue_debate` (two research-debate edges) are single routers whose
 return set is larger than any one edge previously mapped. Each edge now shares a
 complete path map (`RISK_ANALYSIS_PATH_MAP` / `DEBATE_PATH_MAP`), so a
 fall-through return can never hit a missing entry -- which would crash LangGraph
 mid-run on prompt/i18n/refactor drift in the speaker labels.
 """
 import pytest
 from tradingagents.graph.conditional_logic import ConditionalLogic
 from tradingagents.graph.setup import DEBATE_PATH_MAP, RISK_ANALYSIS_PATH_MAP
 def _state(latest_speaker, count=0):
    return {"risk_debate_state": {"latest_speaker": latest_speaker, "count": count}}
 def _debate_state(current_response, count=0):
    return {"investment_debate_state": {"current_response": current_response, "count": count}}
@pytest.mark.unit
@pytest.mark.parametrize("latest_speaker", [
    "Aggressive", "Aggressive Analyst",
    "Conservative", "Conservative Analyst",
    "Neutral", "Neutral Analyst",
    "",                          # drift: empty label
    "Aggressive Risk Analyst",   # drift: node renamed
    "Agresivo",                  # drift: i18n / translated label
 ])
 def test_router_return_always_routable(latest_speaker):
    logic = ConditionalLogic(max_risk_discuss_rounds=1)
    target = logic.should_continue_risk_analysis(_state(latest_speaker))
    assert target in RISK_ANALYSIS_PATH_MAP
@pytest.mark.unit
 def test_router_terminates_at_round_limit():
    logic = ConditionalLogic(max_risk_discuss_rounds=1)
    # count >= 3 * rounds routes to the Portfolio Manager (debate ends)
    assert logic.should_continue_risk_analysis(_state("Neutral", count=3)) == "Portfolio Manager"
@pytest.mark.unit
 def test_path_map_covers_full_router_range():
    logic = ConditionalLogic(max_risk_discuss_rounds=1)
    returns = {
        logic.should_continue_risk_analysis(_state(s, c))
        for s in ("Aggressive", "Conservative", "Neutral", "drift")
        for c in (0, 99)
    }
    # Every value the router can emit is a key in the shared map...
    assert returns <= set(RISK_ANALYSIS_PATH_MAP)
    # ...and the terminal target is reachable.
    assert "Portfolio Manager" in returns
@pytest.mark.unit
@pytest.mark.parametrize("current_response", [
    "Bull", "Bull Researcher", "Bear", "Bear Researcher",
    "",                       # drift: empty label
    "Optimista",              # drift: i18n / translated label
 ])
 def test_debate_router_return_always_routable(current_response):
    logic = ConditionalLogic(max_debate_rounds=1)
    target = logic.should_continue_debate(_debate_state(current_response))
    assert target in DEBATE_PATH_MAP
@pytest.mark.unit
 def test_debate_path_map_covers_full_router_range():
    logic = ConditionalLogic(max_debate_rounds=1)
    returns = {
        logic.should_continue_debate(_debate_state(s, c))
        for s in ("Bull", "Bear", "drift")
        for c in (0, 99)
    }
    assert returns <= set(DEBATE_PATH_MAP)
    assert "Research Manager" in returns  # terminal reachable
@@ -1,5 +1,9 @@
-"""StockTwits fetch degrades (never raises) on transport errors, including the
+"""StockTwits fetch: transport-error resilience (#1024) and crypto symbol
-http.client chunked-transfer exceptions that are not OSErrors (#1024)."""
+mapping (#1113).
 StockTwits lists crypto under ``<BASE>.X`` (Yahoo's ``BTC-USD`` 404s), and any
 transport error must degrade to a placeholder rather than raise.
 """
 from __future__ import annotations
@@ -26,7 +30,7 @@ def _raise(exc):
@pytest.mark.unit
-class StockTwitsResilienceTests:
+class TestStockTwitsResilience:
    @pytest.mark.parametrize(
        "exc",
        [
@@ -40,3 +44,34 @@ class StockTwitsResilienceTests:
            out = stocktwits.fetch_stocktwits_messages("NVDA")
        assert "unavailable" in out.lower()
        assert out.startswith("<stocktwits unavailable")
@pytest.mark.unit
 class TestStockTwitsCryptoSymbols:
    @pytest.mark.parametrize(
        ("ticker", "expected"),
        [
            ("BTC-USD", "BTC.X"),
            ("eth-usd", "ETH.X"),
            ("SOL-USD", "SOL.X"),
            ("BTCUSD", "BTC.X"),      # undashed broker form
            ("BTC-USDT", "BTC.X"),    # stablecoin quote
            ("AMD", "AMD"),
            ("BRK-B", "BRK-B"),       # dashed class share: untouched
            ("GOLD", "GOLD"),         # real equity (aliases elsewhere): untouched here
            ("XYZ-USD", "XYZ-USD"),   # unknown base: not treated as crypto
        ],
    )
    def test_symbol_mapping(self, ticker, expected):
        assert stocktwits._stocktwits_symbol(ticker) == expected
    def test_crypto_pair_requests_dot_x_endpoint(self):
        seen = {}
        def fake_urlopen(req, timeout=None):
            seen["url"] = req.full_url
            raise TimeoutError("stop after capturing the URL")
        with patch.object(stocktwits, "urlopen", side_effect=fake_urlopen):
            stocktwits.fetch_stocktwits_messages("BTC-USD")
        assert "/symbol/BTC.X.json" in seen["url"]
@@ -6,6 +6,7 @@ import pytest
 from tradingagents.dataflows.symbol_utils import (
    NoMarketDataError,
    crypto_base,
    is_yahoo_safe,
    normalize_symbol,
 )
@@ -77,5 +78,25 @@ class TestIsYahooSafe(unittest.TestCase):
            self.assertFalse(is_yahoo_safe(sym))
@pytest.mark.unit
 class TestCryptoBase(unittest.TestCase):
    def test_resolves_known_crypto_forms(self):
        for raw in ("BTC-USD", "BTCUSD", "btc-usdt", "BTC-USDC", "BTCUSD+"):
            self.assertEqual(crypto_base(raw), "BTC")
        self.assertEqual(crypto_base("ETH-USD"), "ETH")
        self.assertEqual(crypto_base("sol-usd"), "SOL")
    def test_non_crypto_returns_none(self):
        # Plain equities, class shares, and real tickers that alias elsewhere
        # (GOLD -> gold future on the Yahoo path) must NOT read as crypto.
        for raw in ("AAPL", "BRK-B", "GOLD", "XYZ-USD", "EURUSD", "", None):
            self.assertIsNone(crypto_base(raw))
    def test_agrees_with_normalize_symbol(self):
        # crypto_base is the shared primitive behind the -USD normalization.
        self.assertEqual(normalize_symbol("BTCUSD"), "BTC-USD")
        self.assertEqual(crypto_base("BTCUSD"), "BTC")
 if __name__ == "__main__":
    unittest.main()
@@ -16,9 +16,12 @@ class TestTemperatureForwarding:
    @pytest.mark.parametrize(
        "provider,model",
        [
            # gpt-4.1 is intentionally a non-reasoning model: the GPT-5 family
            # are reasoning models and correctly drop temperature (see
            # test_openai_reasoning_effort), so forwarding is tested on gpt-4.1.
            ("openai", "gpt-4.1"),
-            ("anthropic", "claude-sonnet-4-6"),
+            ("anthropic", "claude-sonnet-5"),
-            ("google", "gemini-2.5-flash"),
+            ("google", "gemini-3.5-flash"),
            ("deepseek", "deepseek-chat"),
        ],
    )
@@ -25,7 +25,7 @@ def create_news_analyst(llm):
        ]
        system_message = (
-            f"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for {asset_label}-specific or targeted news searches, get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news, get_macro_indicators(indicator, curr_date, look_back_days) to ground macro commentary in actual data from FRED (e.g. 'cpi', 'core_pce', 'unemployment', 'fed_funds_rate', '10y_treasury', 'yield_curve'), and get_prediction_markets(topic, limit) for live market-implied probabilities of forward-looking events (e.g. 'Fed rate cut', 'recession 2026', geopolitical or sector events). Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
+            f"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(ticker, start_date, end_date) for {asset_label}-specific news by ticker symbol, get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news, get_macro_indicators(indicator, curr_date, look_back_days) to ground macro commentary in actual data from FRED (e.g. 'cpi', 'core_pce', 'unemployment', 'fed_funds_rate', '10y_treasury', 'yield_curve'), and get_prediction_markets(topic, limit) for live market-implied probabilities of forward-looking events (e.g. 'Fed rate cut', 'recession 2026', geopolitical or sector events). Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
            + get_language_instruction()
        )
@@ -1,21 +1,30 @@
 import json
 from .alpha_vantage_common import _make_api_request
 def _filter_reports_by_date(result, curr_date: str):
-    """Filter annualReports/quarterlyReports to exclude entries after curr_date.
+    """Drop annual/quarterly reports dated after curr_date to prevent look-ahead.
-    Prevents look-ahead bias by removing fiscal periods that end after
+    ``_make_api_request`` returns the fundamentals payload as a JSON string, so
-    the simulation's current date.
+    parse, filter, and re-serialize. A non-JSON body or an unset ``curr_date`` is
    returned unchanged.
    """
-    if not curr_date or not isinstance(result, dict):
+    if not curr_date or not isinstance(result, str):
        return result
    try:
        payload = json.loads(result)
    except json.JSONDecodeError:
        return result
    if not isinstance(payload, dict):
        return result
    for key in ("annualReports", "quarterlyReports"):
-        if key in result:
+        if isinstance(payload.get(key), list):
-            result[key] = [
+            payload[key] = [
-                r for r in result[key]
+                r for r in payload[key]
                if r.get("fiscalDateEnding", "") <= curr_date
            ]
-    return result
+    return json.dumps(payload)
 def get_fundamentals(ticker: str, curr_date: str = None) -> str:
@@ -35,6 +35,6 @@ def get_stock(
        "datatype": "csv",
    }
-    response = _make_api_request("TIME_SERIES_DAILY_ADJUSTED", params)
+    response = _make_api_request("TIME_SERIES_DAILY", params)
    return _filter_csv_by_date_range(response, start_date, end_date)
@@ -15,23 +15,28 @@ from datetime import datetime, timezone
 import requests
 from .ssh_proxy_pool import ssh_proxy_fetch_json
 logger = logging.getLogger(__name__)
 GAMMA_BASE = "https://gamma-api.polymarket.com"
 # Network timeout (seconds), consistent with the other vendors.
-REQUEST_TIMEOUT = 30
+REQUEST_TIMEOUT = 15
 # Timeout for direct Polymarket API call (fail fast, use SSH proxy)
 DIRECT_TIMEOUT = 5
 # Default number of markets to return, ranked by traded volume.
 DEFAULT_LIMIT = 6
 def _request(path: str, params: dict) -> dict:
-    response = requests.get(
+    url = f"{GAMMA_BASE}/{path}"
-        f"{GAMMA_BASE}/{path}", params=params, timeout=REQUEST_TIMEOUT
+    # Skip local request, go directly through SSH proxy
-    )
+    query = "&".join(f"{k}={v}" for k, v in params.items())
-    response.raise_for_status()
+    full_url = f"{url}?{query}"
-    return response.json()
+    return ssh_proxy_fetch_json(full_url, timeout=REQUEST_TIMEOUT)
 def _parse_json_list(value) -> list:
@@ -30,6 +30,9 @@ from urllib.error import HTTPError
 from urllib.parse import urlencode
 from urllib.request import Request, urlopen
 from .symbol_utils import crypto_base
 from .stockstats_utils import _fetch_via_ssh
 logger = logging.getLogger(__name__)
 _API = "https://www.reddit.com/r/{sub}/search.json?{qs}"
@@ -121,7 +124,12 @@ def _fetch_subreddit_rss(
    except (OSError, http.client.HTTPException, ET.ParseError) as exc:
        # OSError covers URLError/TimeoutError/connection resets; HTTPException
        # covers chunked-transfer errors (IncompleteRead/BadStatusLine, #1024).
-        logger.warning("Reddit RSS fetch failed for r/%s · %s: %s", sub, ticker, exc)
+        logger.warning("Reddit RSS direct fetch failed for r/%s · %s: %s, trying SSH proxy...", sub, ticker, exc)
        try:
            proxy_data = _fetch_via_ssh(url, timeout=timeout)
            root = ET.fromstring(proxy_data.encode() if isinstance(proxy_data, str) else proxy_data)
        except Exception as proxy_exc:
            logger.warning("Reddit RSS proxy also failed for r/%s · %s: %s", sub, ticker, proxy_exc)
            return []
    posts = []
@@ -200,6 +208,9 @@ def fetch_reddit_posts(
    stay under Reddit's public per-IP rate limit; combined with the RSS-first
    path it makes 429s rare even when several analyses run back-to-back.
    """
    # Crypto reaches us as a Yahoo pair (BTC-USD); search Reddit for the base
    # ("BTC") so the query actually matches discussion instead of near-nothing.
    ticker = crypto_base(ticker) or ticker
    blocks = []
    total_posts = 0
    for i, sub in enumerate(subreddits):
@@ -0,0 +1,123 @@
 """SSH Proxy Connection Pool — 串行化所有SSH中转请求，避免并发资源竞争。
 所有需要走中转的数据源统一调用 ssh_proxy_fetch(url)，
 内部通过队列串行化SSH连接，避免多线程/多进程并发导致的连接冲突。
 """
 import json
 import logging
 import subprocess
 import threading
 import time
 from queue import Queue, Empty
 from typing import Optional
 logger = logging.getLogger(__name__)
 # SSH配置缓存
 _ssh_config: Optional[tuple] = None
 _config_lock = threading.Lock()
 def _get_ssh_config() -> tuple:
    """获取SSH配置（host, port, password），带缓存。"""
    global _ssh_config
    if _ssh_config is not None:
        return _ssh_config
    with _config_lock:
        if _ssh_config is not None:
            return _ssh_config
        from .stockstats_utils import _get_ssh_config as _load_config
        _ssh_config = _load_config()
        return _ssh_config
 class SSHProxyPool:
    """SSH代理连接池 — 串行化所有SSH请求。"""
    def __init__(self):
        self._queue = Queue()
        self._lock = threading.Lock()
        self._initialized = False
    def _ensure_initialized(self):
        """确保连接池已初始化。"""
        if self._initialized:
            return
        with self._lock:
            if self._initialized:
                return
            # 放一个占位符，表示可以发起请求
            self._queue.put(True)
            self._initialized = True
    def fetch(self, url: str, timeout: int = 30) -> str:
        """通过SSH代理获取URL内容，串行化执行。
        Args:
            url: 要获取的URL
            timeout: 超时时间（秒）
        Returns:
            URL响应内容字符串
        Raises:
            RuntimeError: SSH代理失败时抛出
        """
        self._ensure_initialized()
        # 等待轮到自己（最多等timeout秒）
        try:
            self._queue.get(timeout=timeout)
        except Empty:
            raise RuntimeError(f"SSH proxy queue timeout after {timeout}s")
        try:
            host, port, password = _get_ssh_config()
            cmd = [
                "sshpass", "-p", password,
                "ssh", "-o", "StrictHostKeyChecking=no",
                "-o", "ConnectTimeout=15",
                "-o", "ServerAliveInterval=10",
                "-p", str(port), f"root@{host}",
                f'curl -sL --max-time {timeout} "{url}"'
            ]
            result = subprocess.run(
                cmd, capture_output=True, text=True, 
                timeout=timeout + 10  # 给SSH额外时间
            )
            if result.returncode != 0:
                raise RuntimeError(f"SSH proxy failed (rc={result.returncode}): {result.stderr[:200]}")
            if not result.stdout or not result.stdout.strip():
                raise RuntimeError("SSH proxy returned empty response")
            return result.stdout
        except subprocess.TimeoutExpired:
            raise RuntimeError(f"SSH proxy subprocess timeout after {timeout}s")
        finally:
            # 无论成功失败，都放回占位符，让下一个请求执行
            self._queue.put(True)
    def fetch_json(self, url: str, timeout: int = 30) -> dict:
        """通过SSH代理获取JSON响应。"""
        text = self.fetch(url, timeout)
        try:
            return json.loads(text)
        except json.JSONDecodeError as e:
            raise RuntimeError(f"SSH proxy returned invalid JSON: {e}")
 # 全局单例
 _pool = SSHProxyPool()
 def ssh_proxy_fetch(url: str, timeout: int = 30) -> str:
    """通过SSH代理获取URL内容（串行化）。"""
    return _pool.fetch(url, timeout)
 def ssh_proxy_fetch_json(url: str, timeout: int = 30) -> dict:
    """通过SSH代理获取JSON响应（串行化）。"""
    return _pool.fetch_json(url, timeout)
@@ -1,6 +1,8 @@
 import logging
 import os
 import time
 import json
 import subprocess
 from typing import Annotated
 import pandas as pd
@@ -19,6 +21,72 @@ logger = logging.getLogger(__name__)
 # enough to catch the year-old frames yfinance occasionally returns (#1021).
 MAX_OHLCV_STALE_DAYS = 10
 _ssh_config_cache = None
 def _get_ssh_config():
    """Load SSH credentials for foreign server from vault (cached)."""
    global _ssh_config_cache
    if _ssh_config_cache is not None:
        return _ssh_config_cache
    result = subprocess.run(
        ["bash", "-c",
         'source <(bash /root/.openclaw/workspace/scripts/vault.sh decrypt '
         '/root/.openclaw/workspace/secrets/credentials.env.enc) '
         '&& echo "$FOREIGN_HOST $FOREIGN_PORT $FOREIGN_PASS"'],
        capture_output=True, text=True, timeout=10
    )
    parts = result.stdout.strip().split()
    if len(parts) != 3:
        raise RuntimeError(f"Failed to load SSH config: {result.stderr}")
    _ssh_config_cache = (parts[0], int(parts[1]), parts[2])
    return _ssh_config_cache
 def _fetch_via_ssh(url: str, timeout: int = 30) -> str:
    """Fetch a URL through the foreign server via SSH tunnel (uses connection pool)."""
    from .ssh_proxy_pool import ssh_proxy_fetch
    return ssh_proxy_fetch(url, timeout)
 def _yf_download_via_proxy(symbol: str, start: str, end: str) -> pd.DataFrame:
    """Download OHLCV data via SSH proxy when local Yahoo Finance is rate-limited."""
    import datetime
    # Convert dates to timestamps
    start_ts = int(pd.Timestamp(start).timestamp())
    end_ts = int(pd.Timestamp(end).timestamp())
    url = (f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}"
           f"?period1={start_ts}&period2={end_ts}&interval=1d")
    data_str = _fetch_via_ssh(url)
    parsed = json.loads(data_str)
    result = parsed.get("chart", {}).get("result", [])
    if not result:
        return pd.DataFrame()
    ts = result[0].get("timestamp", [])
    indicators = result[0].get("indicators", {}).get("quote", [{}])[0]
    if not ts:
        return pd.DataFrame()
    df = pd.DataFrame({
        "Date": pd.to_datetime(ts, unit="s"),
        "Open": indicators.get("open", []),
        "High": indicators.get("high", []),
        "Low": indicators.get("low", []),
        "Close": indicators.get("close", []),
        "Volume": indicators.get("volume", []),
    })
    # Convert None values to NaN
    for col in ["Open", "High", "Low", "Close", "Volume"]:
        df[col] = pd.to_numeric(df[col], errors="coerce")
    df = df.dropna(subset=["Close"])
    return df
 def yf_retry(func, max_retries=3, base_delay=2.0):
    """Execute a yfinance call with exponential backoff on rate limits.
@@ -27,16 +95,8 @@ def yf_retry(func, max_retries=3, base_delay=2.0):
    retry them internally. This wrapper adds retry logic specifically
    for rate limits. Other exceptions propagate immediately.
    """
-    for attempt in range(max_retries + 1):
+    # Skip local Yahoo Finance entirely - always use SSH proxy
-        try:
+    raise YFRateLimitError("Local Yahoo Finance disabled, use SSH proxy")
            return func()
        except YFRateLimitError:
            if attempt < max_retries:
                delay = base_delay * (2 ** attempt)
                logger.warning(f"Yahoo Finance rate limited, retrying in {delay:.0f}s (attempt {attempt + 1}/{max_retries})")
                time.sleep(delay)
            else:
                raise
 def _ensure_date_column(data: pd.DataFrame) -> pd.DataFrame:
@@ -122,6 +182,82 @@ def _assert_ohlcv_not_stale(
        )
 def _load_ohlcv_alpha_vantage(symbol: str, curr_date: str) -> pd.DataFrame:
    """Fetch OHLCV data from Alpha Vantage, filtered to prevent look-ahead bias."""
    from .alpha_vantage_common import _make_api_request
    from io import StringIO
    canonical = normalize_symbol(symbol)
    curr_date_dt = pd.to_datetime(curr_date)
    today_date = pd.Timestamp.today()
    start_date = today_date - pd.DateOffset(years=5)
    start_str = start_date.strftime("%Y-%m-%d")
    # Determine outputsize based on how far back we need
    days_from_today = (today_date - start_date).days
    outputsize = "compact" if days_from_today < 100 else "full"
    params = {
        "symbol": canonical,
        "outputsize": outputsize,
        "datatype": "csv",
    }
    try:
        csv_text = _make_api_request("TIME_SERIES_DAILY", params)
    except Exception as e:
        raise NoMarketDataError(symbol, canonical, f"Alpha Vantage error: {e}") from e
    if not csv_text or not csv_text.strip():
        raise NoMarketDataError(symbol, canonical, "Alpha Vantage returned empty data")
    try:
        df = pd.read_csv(StringIO(csv_text))
    except Exception as e:
        raise NoMarketDataError(symbol, canonical, f"Failed to parse Alpha Vantage CSV: {e}") from e
    if df.empty:
        raise NoMarketDataError(symbol, canonical, "Alpha Vantage returned no rows")
    # Alpha Vantage CSV has 'timestamp' column; normalize to 'Date'
    date_col = df.columns[0]  # usually 'timestamp'
    df = df.rename(columns={date_col: "Date"})
    # Keep only standard OHLCV columns
    keep_cols = ["Date"]
    col_map = {}
    for av_col, std_col in [
        ("open", "Open"), ("high", "High"), ("low", "Low"),
        ("close", "Close"), ("adjusted_close", "Close"), ("volume", "Volume"),
    ]:
        for c in df.columns:
            if c.lower().replace(" ", "_") == av_col:
                if std_col not in keep_cols:
                    keep_cols.append(std_col)
                    col_map[c] = std_col
                break
    df = df.rename(columns=col_map)
    # Prefer adjusted_close over close for "Close"
    if "adjusted_close" in [c.lower().replace(" ", "_") for c in df.columns]:
        for c in df.columns:
            if c.lower().replace(" ", "_") == "adjusted_close":
                df["Close"] = pd.to_numeric(df[c], errors="coerce")
                break
    available = [c for c in ["Date", "Open", "High", "Low", "Close", "Volume"] if c in df.columns]
    df = df[available]
    # Filter to curr_date
    df["Date"] = pd.to_datetime(df["Date"], errors="coerce")
    df = df.dropna(subset=["Date"])
    df = df[df["Date"] <= curr_date_dt].sort_values("Date")
    if df.empty:
        raise NoMarketDataError(symbol, canonical, "No data on or before " + curr_date)
    return df
 def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
    """Fetch OHLCV data with caching, filtered to prevent look-ahead bias.
@@ -138,55 +274,66 @@ def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
    config = get_config()
    curr_date_dt = pd.to_datetime(curr_date)
    # Check which vendor to use for OHLCV
    vendor_config = config.get("data_vendors", {}).get("core_stock_apis", "yfinance")
    use_alpha_vantage = "alpha_vantage" in vendor_config
    # Cache uses a fixed window (5y to today) so one file per symbol.
    today_date = pd.Timestamp.today()
    start_date = today_date - pd.DateOffset(years=5)
    start_str = start_date.strftime("%Y-%m-%d")
    # yfinance ``end`` is EXCLUSIVE; request tomorrow so today's row is included
    # when curr_date is the current day (#986). Look-ahead is still prevented by
    # the curr_date filter below.
    end_str = (today_date + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
    cache_prefix = "AV" if use_alpha_vantage else "YFin"
    os.makedirs(config["data_cache_dir"], exist_ok=True)
    data_file = os.path.join(
        config["data_cache_dir"],
-        f"{safe_symbol}-YFin-data-{start_str}-{end_str}.csv",
+        f"{safe_symbol}-{cache_prefix}-data-{start_str}-{end_str}.csv",
    )
-    # A cached file may be empty if a prior fetch failed (unknown symbol,
+    # A cached file may be empty if a prior fetch failed. Treat empty cache as miss.
    # transient rate limit). Treat an empty/columnless cache as a miss and
    # re-fetch rather than serving the poisoned file forever.
    data = None
    if os.path.exists(data_file):
        cached = pd.read_csv(data_file, on_bad_lines="skip", encoding="utf-8")
        if not cached.empty and "Close" in cached.columns:
            # Ensure Date column is datetime after loading from cache
            if "Date" in cached.columns:
                cached["Date"] = pd.to_datetime(cached["Date"], errors="coerce")
                cached = cached.dropna(subset=["Date"])
            data = cached
    if data is None:
-        downloaded = yf_retry(lambda: yf.download(
+        if use_alpha_vantage:
-            canonical,
+            downloaded = _load_ohlcv_alpha_vantage(symbol, curr_date)
-            start=start_str,
+        else:
-            end=end_str,
+            # Skip local Yahoo Finance, go directly through SSH proxy
-            multi_level_index=False,
+            logger.info(f"Yahoo Finance: fetching {canonical} via SSH proxy...")
-            progress=False,
+            try:
-            auto_adjust=True,
+                downloaded = _yf_download_via_proxy(canonical, start_str, end_str)
-        ))
+            except Exception as proxy_err:
-        downloaded = _ensure_date_column(downloaded.reset_index())
+                raise NoMarketDataError(
-        # Only cache real data — never persist an empty frame.
+                    symbol, canonical, f"Yahoo Finance proxy failed: {proxy_err}"
                ) from proxy_err
            if downloaded.empty:
                raise NoMarketDataError(
                    symbol, canonical, f"Yahoo Finance proxy returned no data"
                )
            if downloaded.empty or "Close" not in downloaded.columns:
                raise NoMarketDataError(
                    symbol, canonical, "Yahoo Finance returned no rows"
                )
            downloaded = _clean_dataframe(downloaded)
        downloaded.to_csv(data_file, index=False, encoding="utf-8")
        data = downloaded
    if not use_alpha_vantage:
        data = _clean_dataframe(data)
    # Filter to curr_date to prevent look-ahead bias in backtesting
    data = data[data["Date"] <= curr_date_dt]
-    # Reject a stale frame (latest row far older than curr_date) rather than
+    # Reject a stale frame
    # feeding year-old prices into indicators (#1021).
    _assert_ohlcv_not_stale(data, curr_date, symbol, canonical)
    return data
@@ -19,12 +19,26 @@ import json
 import logging
 from urllib.request import Request, urlopen
 from .symbol_utils import crypto_base
 from .stockstats_utils import _fetch_via_ssh
 logger = logging.getLogger(__name__)
 _API = "https://api.stocktwits.com/api/2/streams/symbol/{ticker}.json"
 _UA = "tradingagents/0.2 (+https://github.com/TauricResearch/TradingAgents)"
 def _stocktwits_symbol(ticker: str) -> str:
    """Map a crypto pair to StockTwits' ``<BASE>.X`` convention.
    StockTwits lists crypto as ``BTC.X`` (Yahoo's ``BTC-USD`` form 404s), so any
    crypto symbol resolves to its base plus ``.X``; other symbols pass through
    upper-cased.
    """
    base = crypto_base(ticker)
    return f"{base}.X" if base else ticker.strip().upper()
 def fetch_stocktwits_messages(ticker: str, limit: int = 30, timeout: float = 10.0) -> str:
    """Fetch recent StockTwits messages for ``ticker`` and return them as a
    formatted plaintext block ready for prompt injection.
@@ -33,7 +47,7 @@ def fetch_stocktwits_messages(ticker: str, limit: int = 30, timeout: float = 10.
    symbol has no messages, or the response shape is unexpected — the
    caller never has to special-case None or exceptions.
    """
-    url = _API.format(ticker=ticker.upper())
+    url = _API.format(ticker=_stocktwits_symbol(ticker))
    req = Request(url, headers={"User-Agent": _UA, "Accept": "application/json"})
    try:
        with urlopen(req, timeout=timeout) as resp:
@@ -41,7 +55,12 @@ def fetch_stocktwits_messages(ticker: str, limit: int = 30, timeout: float = 10.
    except (OSError, http.client.HTTPException, json.JSONDecodeError) as exc:
        # OSError covers URLError/TimeoutError/connection resets; HTTPException
        # covers chunked-transfer errors (IncompleteRead/BadStatusLine, #1024).
-        logger.warning("StockTwits fetch failed for %s: %s", ticker, exc)
+        logger.warning("StockTwits direct fetch failed for %s: %s, trying SSH proxy...", ticker, exc)
        try:
            proxy_data = _fetch_via_ssh(url, timeout=timeout)
            data = json.loads(proxy_data)
        except Exception as proxy_exc:
            logger.warning("StockTwits proxy also failed for %s: %s", ticker, proxy_exc)
            return f"<stocktwits unavailable: {type(exc).__name__}>"
    messages = data.get("messages", []) if isinstance(data, dict) else []
@@ -80,22 +80,27 @@ _YAHOO_SAFE = re.compile(r"^[A-Za-z0-9._\-\^=]+$")
 _CRYPTO_QUOTES = ("USDT", "USDC", "USD")
-def _normalize_crypto(s: str) -> str | None:
+def crypto_base(raw: str) -> str | None:
-    """Return ``<BASE>-USD`` if ``s`` is a known crypto quoted in USD/USDT/USDC.
+    """Return the crypto base (e.g. ``BTC``) for a known USD/USDT/USDC-quoted
-
+    crypto symbol in any form the pipeline may hold — ``BTC-USD``, ``BTCUSD``,
-    Accepts dashed or undashed forms: ``BTCUSD``, ``BTCUSDT``, ``BTC-USDT``,
+    ``BTC-USDT`` — or None for non-crypto symbols. Purely syntactic.
    ``BTC-USDC`` all resolve to ``BTC-USD``. Returns None otherwise.
    """
-    compact = s.replace("-", "")
+    if not isinstance(raw, str):
        return None
    compact = raw.strip().upper().rstrip("+").replace("-", "")
    for quote in _CRYPTO_QUOTES:
        if compact.endswith(quote):
            base = compact[: -len(quote)]
-            if base in _CRYPTO_BASES:
+            return base if base in _CRYPTO_BASES else None
                return f"{base}-USD"
            break
    return None
 def _normalize_crypto(s: str) -> str | None:
    """Return ``<BASE>-USD`` for a known USD/USDT/USDC-quoted crypto, else None."""
    base = crypto_base(s)
    return f"{base}-USD" if base else None
 def normalize_symbol(raw: str) -> str:
    """Map a user/broker symbol to its canonical Yahoo Finance symbol.
@@ -1,13 +1,17 @@
 import logging
 from datetime import datetime
 from typing import Annotated
 import pandas as pd
 import yfinance as yf
 logger = logging.getLogger(__name__)
 from dateutil.relativedelta import relativedelta
 from .stockstats_utils import (
    StockstatsUtils,
    _assert_ohlcv_not_stale,
    _yf_download_via_proxy,
    filter_financials_by_date,
    load_ohlcv,
    yf_retry,
@@ -32,7 +36,15 @@ def get_YFin_data_online(
    # end_date row (and the current day when end_date is today). Request one day
    # past end_date so the requested range is actually inclusive (#986/#987).
    end_inclusive = (end_dt + relativedelta(days=1)).strftime("%Y-%m-%d")
-    data = yf_retry(lambda: ticker.history(start=start_date, end=end_inclusive))
+    # Skip local Yahoo Finance (rate-limited), go directly through SSH proxy
    logger.info(f"Yahoo Finance: fetching {canonical} via SSH proxy...")
    proxy_df = _yf_download_via_proxy(canonical, start_date, end_inclusive)
    if proxy_df.empty:
        raise NoMarketDataError(
            symbol, canonical, f"no rows between {start_date} and {end_date} (proxy failed)"
        )
    proxy_df = proxy_df.set_index("Date")
    data = proxy_df
    # Empty result means the symbol is unknown/delisted. Raise a typed error
    # instead of returning prose: the routing layer turns it into a single
@@ -0,0 +1,53 @@
 #!/usr/bin/env python3
 """Yahoo Finance proxy via foreign server.
 Routes yfinance requests through the foreign server to bypass local IP rate limiting.
 """
 import subprocess
 import json
 import os
 import sys
 def _get_ssh_config():
    """Load SSH credentials from vault."""
    result = subprocess.run(
        ["bash", "-c", 'source <(bash /root/.openclaw/workspace/scripts/vault.sh decrypt /root/.openclaw/workspace/secrets/credentials.env.enc) && echo "$FOREIGN_HOST $FOREIGN_PORT $FOREIGN_PASS"'],
        capture_output=True, text=True, timeout=10
    )
    parts = result.stdout.strip().split()
    if len(parts) != 3:
        raise RuntimeError(f"Failed to load SSH config: {result.stderr}")
    return parts[0], int(parts[1]), parts[2]
 def fetch_yahoo_via_ssh(url: str, timeout: int = 30) -> str:
    """Fetch a Yahoo Finance URL through the foreign server via SSH."""
    host, port, password = _get_ssh_config()
    cmd = [
        "sshpass", "-p", password,
        "ssh", "-o", "StrictHostKeyChecking=no",
        "-o", f"ConnectTimeout=15",
        "-p", str(port), f"root@{host}",
        f'curl -s "{url}" -H "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"'
    ]
    result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
    if result.returncode != 0:
        raise RuntimeError(f"SSH fetch failed: {result.stderr}")
    return result.stdout
 def test_connection() -> bool:
    """Test if Yahoo Finance is accessible via foreign server."""
    try:
        data = fetch_yahoo_via_ssh(
            "https://query2.finance.yahoo.com/v8/finance/chart/AAPL?interval=1d&range=5d"
        )
        parsed = json.loads(data)
        return "chart" in parsed and "result" in parsed.get("chart", {})
    except Exception as e:
        print(f"Connection test failed: {e}", file=sys.stderr)
        return False
 if __name__ == "__main__":
    if test_connection():
        print("✅ Yahoo Finance proxy via foreign server: OK")
    else:
        print("❌ Yahoo Finance proxy via foreign server: FAILED")
@@ -18,6 +18,7 @@ _ENV_OVERRIDES = {
    "TRADINGAGENTS_CHECKPOINT_ENABLED":   "checkpoint_enabled",
    "TRADINGAGENTS_BENCHMARK_TICKER":     "benchmark_ticker",
    "TRADINGAGENTS_TEMPERATURE":          "temperature",
    "TRADINGAGENTS_LLM_MAX_RETRIES":      "llm_max_retries",
    # Provider-specific reasoning/thinking knobs (None = each provider's own
    # default). Settable here for non-interactive runs; the CLI also offers an
    # interactive choice, which is skipped when the matching var is set.
@@ -95,6 +96,10 @@ DEFAULT_CONFIG = _apply_env_overrides({
    # variation on models that honor it; reasoning models largely ignore it
    # and no setting makes LLM output bit-identical across runs (see README).
    "temperature": None,
    # SDK retry budget forwarded to every provider chat client. None leaves each
    # provider/SDK at its own default (usually 2). Raise it to ride out bursty
    # 429 throttling on rate-limited deployments instead of aborting a run (#1091).
    "llm_max_retries": None,
    # Checkpoint/resume: when True, LangGraph saves state after each node
    # so a crashed run can resume from the last successful step.
    "checkpoint_enabled": False,
@@ -25,9 +25,17 @@ def _db_path(data_dir: str | Path, ticker: str) -> Path:
    return p / f"{safe}.db"
-def thread_id(ticker: str, date: str) -> str:
+def thread_id(ticker: str, date: str, signature: str = "") -> str:
-    """Deterministic thread ID for a ticker+date pair."""
+    """Deterministic thread ID for a ticker+date pair.
-    return hashlib.sha256(f"{ticker.upper()}:{date}".encode()).hexdigest()[:16]
+
    ``signature`` folds in graph-shape-affecting run choices so a resume under a
    different graph can't reuse this checkpoint (#1089); omitting it keeps the
    legacy ID.
    """
    base = f"{ticker.upper()}:{date}"
    if signature:
        base = f"{base}:{signature}"
    return hashlib.sha256(base.encode()).hexdigest()[:16]
@contextmanager
@@ -43,17 +51,17 @@ def get_checkpointer(data_dir: str | Path, ticker: str) -> Generator[SqliteSaver
        conn.close()
-def has_checkpoint(data_dir: str | Path, ticker: str, date: str) -> bool:
+def has_checkpoint(data_dir: str | Path, ticker: str, date: str, signature: str = "") -> bool:
    """Check whether a resumable checkpoint exists for ticker+date."""
-    return checkpoint_step(data_dir, ticker, date) is not None
+    return checkpoint_step(data_dir, ticker, date, signature) is not None
-def checkpoint_step(data_dir: str | Path, ticker: str, date: str) -> int | None:
+def checkpoint_step(data_dir: str | Path, ticker: str, date: str, signature: str = "") -> int | None:
    """Return the step number of the latest checkpoint, or None if none exists."""
    db = _db_path(data_dir, ticker)
    if not db.exists():
        return None
-    tid = thread_id(ticker, date)
+    tid = thread_id(ticker, date, signature)
    with get_checkpointer(data_dir, ticker) as saver:
        config = {"configurable": {"thread_id": tid}}
        cp = saver.get_tuple(config)
@@ -73,12 +81,12 @@ def clear_all_checkpoints(data_dir: str | Path) -> int:
    return len(dbs)
-def clear_checkpoint(data_dir: str | Path, ticker: str, date: str) -> None:
+def clear_checkpoint(data_dir: str | Path, ticker: str, date: str, signature: str = "") -> None:
    """Remove checkpoint for a specific ticker+date by deleting the thread's rows."""
    db = _db_path(data_dir, ticker)
    if not db.exists():
        return
-    tid = thread_id(ticker, date)
+    tid = thread_id(ticker, date, signature)
    conn = sqlite3.connect(str(db))
    try:
        for table in ("writes", "checkpoints"):
@@ -25,6 +25,22 @@ from tradingagents.agents.utils.agent_states import AgentState
 from .analyst_execution import build_analyst_execution_plan
 from .conditional_logic import ConditionalLogic
 # Every target a shared conditional router can return. Each edge driven by the
 # router maps all of them, so a fall-through return (e.g. under prompt/i18n/
 # refactor drift in the speaker labels) can never hit a missing path_map entry
 # and crash LangGraph mid-run (#1088).
 DEBATE_PATH_MAP = {
    "Bull Researcher": "Bull Researcher",
    "Bear Researcher": "Bear Researcher",
    "Research Manager": "Research Manager",
 }
 RISK_ANALYSIS_PATH_MAP = {
    "Aggressive Analyst": "Aggressive Analyst",
    "Conservative Analyst": "Conservative Analyst",
    "Neutral Analyst": "Neutral Analyst",
    "Portfolio Manager": "Portfolio Manager",
 }
 class GraphSetup:
    """Handles the setup and configuration of the agent graph."""
@@ -118,48 +134,21 @@ class GraphSetup:
            else:
                workflow.add_edge(current_clear, "Bull Researcher")
-        # Add remaining edges
+        # Both research-debate edges share the complete DEBATE_PATH_MAP (#1088).
        for debate_node in ("Bull Researcher", "Bear Researcher"):
            workflow.add_conditional_edges(
-            "Bull Researcher",
+                debate_node,
                self.conditional_logic.should_continue_debate,
-            {
+                DEBATE_PATH_MAP,
                "Bear Researcher": "Bear Researcher",
                "Research Manager": "Research Manager",
            },
        )
        workflow.add_conditional_edges(
            "Bear Researcher",
            self.conditional_logic.should_continue_debate,
            {
                "Bull Researcher": "Bull Researcher",
                "Research Manager": "Research Manager",
            },
            )
        workflow.add_edge("Research Manager", "Trader")
        workflow.add_edge("Trader", "Aggressive Analyst")
        # All three risk edges share the complete RISK_ANALYSIS_PATH_MAP (#1088).
        for risk_node in ("Aggressive Analyst", "Conservative Analyst", "Neutral Analyst"):
            workflow.add_conditional_edges(
-            "Aggressive Analyst",
+                risk_node,
                self.conditional_logic.should_continue_risk_analysis,
-            {
+                RISK_ANALYSIS_PATH_MAP,
                "Conservative Analyst": "Conservative Analyst",
                "Portfolio Manager": "Portfolio Manager",
            },
        )
        workflow.add_conditional_edges(
            "Conservative Analyst",
            self.conditional_logic.should_continue_risk_analysis,
            {
                "Neutral Analyst": "Neutral Analyst",
                "Portfolio Manager": "Portfolio Manager",
            },
        )
        workflow.add_conditional_edges(
            "Neutral Analyst",
            self.conditional_logic.should_continue_risk_analysis,
            {
                "Aggressive Analyst": "Aggressive Analyst",
                "Portfolio Manager": "Portfolio Manager",
            },
            )
        workflow.add_edge("Portfolio Manager", END)
@@ -44,6 +44,24 @@ from .signal_processing import SignalProcessor
 logger = logging.getLogger(__name__)
 def _coerce_max_retries(value):
    """Validate an ``llm_max_retries`` value to a non-negative int.
    Accepts an int or a numeric string (env vars arrive as strings). Rejects
    booleans and negatives loudly so a misconfiguration fails at startup rather
    than silently disabling retries.
    """
    if isinstance(value, bool):
        raise ValueError(f"llm_max_retries must be an integer, not a boolean: {value!r}")
    try:
        n = int(value)
    except (TypeError, ValueError) as exc:
        raise ValueError(f"llm_max_retries must be an integer, got {value!r}") from exc
    if n < 0:
        raise ValueError(f"llm_max_retries must be >= 0, got {n}")
    return n
 class TradingAgentsGraph:
    """Main class that orchestrates the trading agents framework."""
@@ -124,6 +142,9 @@ class TradingAgentsGraph:
        self.ticker = None
        self.log_states_dict = {}  # date to full state dict
        # Graph-shape-affecting run choices, kept for the checkpoint signature.
        self.selected_analysts = tuple(selected_analysts)
        # Set up the graph: keep the workflow for recompilation with a checkpointer.
        self.workflow = self.graph_setup.setup_graph(selected_analysts)
        self.graph = self.workflow.compile()
@@ -156,6 +177,12 @@ class TradingAgentsGraph:
        if temperature is not None and temperature != "":
            kwargs["temperature"] = float(temperature)
        # SDK retry budget is cross-provider. Forward it only when explicitly set
        # so each provider keeps its own default (usually 2) otherwise (#1091).
        max_retries = self.config.get("llm_max_retries")
        if max_retries is not None and max_retries != "":
            kwargs["max_retries"] = _coerce_max_retries(max_retries)
        return kwargs
    def _create_tool_nodes(self) -> dict[str, ToolNode]:
@@ -318,6 +345,20 @@ class TradingAgentsGraph:
        identity = resolve_instrument_identity(ticker)
        return build_instrument_context(ticker, asset_type, identity)
    def _run_signature(self, asset_type: str) -> str:
        """Graph-shape inputs that must invalidate a checkpoint if changed.
        Keyed into the checkpoint thread ID so a resume under a different analyst
        selection, debate/risk depth, or asset mode starts fresh instead of
        silently continuing the previous graph (#1089).
        """
        return "|".join([
            "analysts=" + ",".join(self.selected_analysts),
            f"debate={self.config['max_debate_rounds']}",
            f"risk={self.config['max_risk_discuss_rounds']}",
            f"asset={asset_type}",
        ])
    def propagate(self, company_name, trade_date, asset_type: str = "stock"):
        """Run the trading agents graph for a company on a specific date.
@@ -342,7 +383,8 @@ class TradingAgentsGraph:
            self.graph = self.workflow.compile(checkpointer=saver)
            step = checkpoint_step(
-                self.config["data_cache_dir"], company_name, str(trade_date)
+                self.config["data_cache_dir"], company_name, str(trade_date),
                self._run_signature(asset_type),
            )
            if step is not None:
                logger.info(
@@ -389,9 +431,10 @@ class TradingAgentsGraph:
        )
        args = self.propagator.get_graph_args()
-        # Inject thread_id so same ticker+date resumes, different date starts fresh.
+        # Inject thread_id so same ticker+date+graph-shape resumes; a different
        # date or graph shape starts fresh (#1089).
        if self.config.get("checkpoint_enabled"):
-            tid = thread_id(company_name, str(trade_date))
+            tid = thread_id(company_name, str(trade_date), self._run_signature(asset_type))
            args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = tid
        if self.debug:
@@ -432,7 +475,8 @@ class TradingAgentsGraph:
        # Clear checkpoint on successful completion to avoid stale state.
        if self.config.get("checkpoint_enabled"):
            clear_checkpoint(
-                self.config["data_cache_dir"], company_name, str(trade_date)
+                self.config["data_cache_dir"], company_name, str(trade_date),
                self._run_signature(asset_type),
            )
        return final_state, self.process_signal(final_state["final_trade_decision"])
@@ -11,16 +11,17 @@ _PASSTHROUGH_KWARGS = (
    "callbacks", "http_client", "http_async_client", "effort",
 )
-# Anthropic's extended-thinking ``effort`` parameter is accepted by Opus 4.5+
+# Anthropic's extended-thinking ``effort`` parameter is accepted by Opus 4.5+,
-# and Sonnet 4.6+ only. Sonnet 4.5 and any Haiku version 400 with
+# Sonnet 4.6+, and the Claude 5 family (Sonnet 5, Fable 5). Sonnet 4.5 and any
-# ``"This model does not support the effort parameter"`` (#831). The per-family
+# Haiku version 400 with ``"This model does not support the effort parameter"``
-# minimum version below is forward-compatible: future ``claude-{opus,sonnet}-X-Y``
+# (#831). Versions may be dotted (``opus-4-8``) or single-number (``sonnet-5``,
-# releases inherit support automatically, while Sonnet 4.5 and Haiku stay excluded.
+# ``fable-5``); the per-family minimum below is forward-compatible.
 _EFFORT_EXACT = {
    "claude-mythos-preview",  # non-standard preview name; effort-capable
    "claude-mythos-5",        # Fable 5 twin (Project Glasswing); effort-capable
 }
-_EFFORT_MODEL = re.compile(r"^claude-(opus|sonnet)-(\d+)-(\d+)$")
+_EFFORT_MODEL = re.compile(r"^claude-(opus|sonnet|fable)-(\d+)(?:-(\d+))?$")
-_EFFORT_MIN_VERSION = {"opus": (4, 5), "sonnet": (4, 6)}
+_EFFORT_MIN_VERSION = {"opus": (4, 5), "sonnet": (4, 6), "fable": (5, 0)}
 def _supports_effort(model: str) -> bool:
@@ -31,7 +32,9 @@ def _supports_effort(model: str) -> bool:
    match = _EFFORT_MODEL.match(model_lc)
    if not match:
        return False
-    family, major, minor = match.group(1), int(match.group(2)), int(match.group(3))
+    family = match.group(1)
    major = int(match.group(2))
    minor = int(match.group(3)) if match.group(3) else 0
    return (major, minor) >= _EFFORT_MIN_VERSION[family]
@@ -41,11 +41,12 @@ def _bedrock_class():
 class BedrockClient(BaseLLMClient):
    """Client for Amazon Bedrock via the Converse API (langchain-aws).
-    Authentication uses the standard AWS credential chain (env vars,
+    Authentication is either a Bedrock API key (bearer token) via
-    ``~/.aws/credentials``, or an IAM role); set ``AWS_REGION`` /
+    ``AWS_BEARER_TOKEN_BEDROCK`` — no AWS access keys required — or the standard
-    ``AWS_DEFAULT_REGION`` and optionally ``AWS_PROFILE``. The model name is a
+    AWS credential chain (env vars, ``~/.aws/credentials``, or an IAM role) with
-    Bedrock model ID or cross-region inference profile ID, e.g.
+    optional ``AWS_PROFILE``. Set ``AWS_REGION`` / ``AWS_DEFAULT_REGION`` either
-    ``us.anthropic.claude-opus-4-8-v1:0``.
+    way (the token carries no region). The model name is a Bedrock model ID or
    cross-region inference profile ID, e.g. ``us.anthropic.claude-opus-4-8-v1:0``.
    """
    def get_llm(self) -> Any:
@@ -59,6 +60,12 @@ class BedrockClient(BaseLLMClient):
            or _DEFAULT_REGION
        )
        llm_kwargs = {"model": self.model, "region_name": region}
        # A Bedrock API key authenticates without AWS access keys. Passing it as
        # api_key makes langchain-aws prefer bearer auth, so an ambient
        # AWS_PROFILE / SigV4 credentials can't override it (#1103).
        bearer_token = os.environ.get("AWS_BEARER_TOKEN_BEDROCK")
        if bearer_token:
            llm_kwargs["api_key"] = bearer_token
        for key in ("temperature", "max_tokens", "max_retries", "callbacks"):
            if key in self.kwargs:
                llm_kwargs[key] = self.kwargs[key]
@@ -94,14 +94,14 @@ MODEL_OPTIONS: ProviderModeOptions = {
    },
    "anthropic": {
        "quick": [
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
+            ("Claude Sonnet 5 - Best speed and intelligence balance", "claude-sonnet-5"),
            ("Claude Haiku 4.5 - Fastest with near-frontier intelligence", "claude-haiku-4-5"),
        ],
        "deep": [
-            ("Claude Opus 4.8 - Latest frontier, agentic coding and reasoning", "claude-opus-4-8"),
+            ("Claude Fable 5 - Most capable, long-running agents", "claude-fable-5"),
            ("Claude Opus 4.8 - Frontier agentic coding and reasoning", "claude-opus-4-8"),
            ("Claude Sonnet 5 - Near-frontier intelligence at Sonnet cost", "claude-sonnet-5"),
            ("Claude Opus 4.7 - Previous frontier, long-running agents", "claude-opus-4-7"),
            ("Claude Opus 4.6 - Frontier intelligence, agents and coding", "claude-opus-4-6"),
            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
        ],
    },
    "google": {
@@ -33,7 +33,21 @@ class NormalizedChatOpenAI(ChatOpenAI):
    """
    def invoke(self, input, config=None, **kwargs):
        import json as _json
        import time as _time
        import logging as _logging
        _logger = _logging.getLogger(__name__)
        max_retries = 3
        for attempt in range(max_retries):
            try:
                return normalize_content(super().invoke(input, config, **kwargs))
            except (_json.JSONDecodeError, Exception) as e:
                if 'JSONDecodeError' in type(e).__name__ or 'Expecting value' in str(e):
                    if attempt < max_retries - 1:
                        _logger.warning(f"LLM returned invalid JSON, retrying ({attempt+1}/{max_retries}): {e}")
                        _time.sleep(2 * (attempt + 1))
                        continue
                raise
    def with_structured_output(self, schema, *, method=None, **kwargs):
        caps = get_capabilities(self.model_name)
@@ -0,0 +1,640 @@
 """TradingAgents Web 可视化页面
 提供Web界面让用户输入股票代码，运行TradingAgents分析，查看结果。
 """
 import json
 import os
 import sys
 import threading
 import uuid
 from datetime import datetime
 from flask import Flask, render_template_string, request, jsonify
 # 确保项目根目录在路径中
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 app = Flask(__name__)
 # 存储运行中的任务
 tasks = {}
 # HTML模板
 HTML_TEMPLATE = """
 <!DOCTYPE html>
 <html lang="zh-CN">
 <head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>TradingAgents AI 交易分析</title>
    <style>
        * { margin: 0; padding: 0; box-sizing: border-box; }
        body { 
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
            background: linear-gradient(135deg, #0a0a2e 0%, #1a1a4e 50%, #0d0d3d 100%);
            color: #e0e0e0;
            min-height: 100vh;
        }
        .container { max-width: 1200px; margin: 0 auto; padding: 20px; }
        /* Header */
        .header {
            text-align: center;
            padding: 40px 0 30px;
        }
        .header h1 {
            font-size: 2.5em;
            background: linear-gradient(120deg, #00d4ff, #7c3aed, #f472b6);
            -webkit-background-clip: text;
            -webkit-text-fill-color: transparent;
            margin-bottom: 10px;
        }
        .header p {
            color: #8888aa;
            font-size: 1.1em;
        }
        /* Input Form */
        .form-card {
            background: rgba(255,255,255,0.05);
            border: 1px solid rgba(255,255,255,0.1);
            border-radius: 16px;
            padding: 30px;
            margin-bottom: 30px;
            backdrop-filter: blur(10px);
        }
        .form-row {
            display: flex;
            gap: 15px;
            align-items: flex-end;
            flex-wrap: wrap;
        }
        .form-group {
            flex: 1;
            min-width: 200px;
        }
        .form-group label {
            display: block;
            margin-bottom: 8px;
            color: #aaaacc;
            font-size: 0.9em;
        }
        .form-group input, .form-group select {
            width: 100%;
            padding: 12px 16px;
            background: rgba(255,255,255,0.08);
            border: 1px solid rgba(255,255,255,0.15);
            border-radius: 10px;
            color: #fff;
            font-size: 1em;
            transition: all 0.3s;
        }
        .form-group input:focus, .form-group select:focus {
            outline: none;
            border-color: #7c3aed;
            box-shadow: 0 0 20px rgba(124, 58, 237, 0.3);
        }
        .form-group input::placeholder { color: #666688; }
        .btn {
            padding: 12px 30px;
            border: none;
            border-radius: 10px;
            font-size: 1em;
            font-weight: 600;
            cursor: pointer;
            transition: all 0.3s;
        }
        .btn-primary {
            background: linear-gradient(135deg, #7c3aed, #3b82f6);
            color: white;
        }
        .btn-primary:hover {
            transform: translateY(-2px);
            box-shadow: 0 8px 25px rgba(124, 58, 237, 0.4);
        }
        .btn-primary:disabled {
            opacity: 0.5;
            cursor: not-allowed;
            transform: none;
        }
        /* Preset Buttons */
        .presets {
            display: flex;
            gap: 10px;
            margin-top: 15px;
            flex-wrap: wrap;
        }
        .preset-btn {
            padding: 6px 14px;
            background: rgba(255,255,255,0.06);
            border: 1px solid rgba(255,255,255,0.1);
            border-radius: 20px;
            color: #aaaacc;
            font-size: 0.85em;
            cursor: pointer;
            transition: all 0.3s;
        }
        .preset-btn:hover {
            background: rgba(124, 58, 237, 0.2);
            border-color: #7c3aed;
            color: #fff;
        }
        /* Status */
        .status-card {
            background: rgba(255,255,255,0.03);
            border: 1px solid rgba(255,255,255,0.08);
            border-radius: 12px;
            padding: 20px;
            margin-bottom: 20px;
            display: none;
        }
        .status-card.active { display: block; }
        .status-header {
            display: flex;
            align-items: center;
            gap: 10px;
            margin-bottom: 15px;
        }
        .spinner {
            width: 20px;
            height: 20px;
            border: 2px solid rgba(124, 58, 237, 0.3);
            border-top-color: #7c3aed;
            border-radius: 50%;
            animation: spin 1s linear infinite;
        }
        @keyframes spin { to { transform: rotate(360deg); } }
        .log-box {
            background: rgba(0,0,0,0.3);
            border-radius: 8px;
            padding: 15px;
            max-height: 200px;
            overflow-y: auto;
            font-family: 'Courier New', monospace;
            font-size: 0.85em;
            color: #8888aa;
            line-height: 1.6;
        }
        .log-box .info { color: #3b82f6; }
        .log-box .success { color: #10b981; }
        .log-box .error { color: #ef4444; }
        /* Results */
        .results-card {
            background: rgba(255,255,255,0.05);
            border: 1px solid rgba(255,255,255,0.1);
            border-radius: 16px;
            padding: 30px;
            margin-bottom: 20px;
            display: none;
        }
        .results-card.active { display: block; }
        .result-section {
            margin-bottom: 25px;
            padding-bottom: 25px;
            border-bottom: 1px solid rgba(255,255,255,0.05);
        }
        .result-section:last-child { border-bottom: none; margin-bottom: 0; padding-bottom: 0; }
        .result-section h3 {
            color: #7c3aed;
            margin-bottom: 15px;
            font-size: 1.2em;
        }
        .decision-box {
            background: linear-gradient(135deg, rgba(124, 58, 237, 0.15), rgba(59, 130, 246, 0.15));
            border: 1px solid rgba(124, 58, 237, 0.3);
            border-radius: 12px;
            padding: 25px;
            text-align: center;
            margin-bottom: 20px;
        }
        .decision-label {
            font-size: 0.9em;
            color: #8888aa;
            margin-bottom: 10px;
        }
        .decision-value {
            font-size: 2em;
            font-weight: bold;
        }
        .decision-buy { color: #10b981; }
        .decision-sell { color: #ef4444; }
        .decision-hold { color: #f59e0b; }
        .agent-report {
            background: rgba(255,255,255,0.03);
            border: 1px solid rgba(255,255,255,0.08);
            border-radius: 10px;
            padding: 20px;
            margin-bottom: 15px;
        }
        .agent-report h4 {
            color: #3b82f6;
            margin-bottom: 12px;
            font-size: 1.05em;
        }
        .agent-report pre {
            white-space: pre-wrap;
            word-wrap: break-word;
            font-size: 0.9em;
            line-height: 1.6;
            color: #ccccee;
        }
        /* Footer */
        .footer {
            text-align: center;
            padding: 30px 0;
            color: #555577;
            font-size: 0.85em;
        }
        /* Responsive */
        @media (max-width: 768px) {
            .header h1 { font-size: 1.8em; }
            .form-row { flex-direction: column; }
            .form-group { min-width: 100%; }
        }
    </style>
 </head>
 <body>
    <div class="container">
        <div class="header">
            <h1>🤖 TradingAgents</h1>
            <p>AI 多智能体交易分析系统 · Powered by MiMo-v2.5-pro</p>
        </div>
        <div class="form-card">
            <form id="analysisForm">
                <div class="form-row">
                    <div class="form-group">
                        <label>📊 股票代码</label>
                        <input type="text" id="ticker" placeholder="例: AAPL, 600519.SS, BTC-USD" required>
                    </div>
                    <div class="form-group" style="max-width: 200px;">
                        <label>📅 分析日期</label>
                        <input type="date" id="date" value="{{ default_date }}">
                    </div>
                    <div class="form-group" style="max-width: 150px;">
                        <label>&nbsp;</label>
                        <button type="submit" class="btn btn-primary" id="submitBtn">
                            🚀 开始分析
                        </button>
                    </div>
                </div>
                <div class="presets">
                    <span class="preset-btn" onclick="setTicker('AAPL')">🍎 AAPL</span>
                    <span class="preset-btn" onclick="setTicker('NVDA')">💚 NVDA</span>
                    <span class="preset-btn" onclick="setTicker('600519.SS')">🍷 茅台</span>
                    <span class="preset-btn" onclick="setTicker('000001.SZ')">🏦 平安</span>
                    <span class="preset-btn" onclick="setTicker('0700.HK')">🐧 腾讯</span>
                    <span class="preset-btn" onclick="setTicker('BTC-USD')">₿ BTC</span>
                    <span class="preset-btn" onclick="setTicker('SPY')">📈 SPY</span>
                </div>
            </form>
        </div>
        <div class="status-card" id="statusCard">
            <div class="status-header">
                <div class="spinner"></div>
                <span id="statusText">正在初始化分析...</span>
            </div>
            <div class="log-box" id="logBox"></div>
        </div>
        <div class="results-card" id="resultsCard">
            <div class="decision-box">
                <div class="decision-label">📊 交易决策</div>
                <div class="decision-value" id="decisionValue">--</div>
            </div>
            <div class="result-section">
                <h3>📈 技术分析师</h3>
                <div class="agent-report"><pre id="techReport">等待分析...</pre></div>
            </div>
            <div class="result-section">
                <h3>💭 情绪分析师</h3>
                <div class="agent-report"><pre id="sentimentReport">等待分析...</pre></div>
            </div>
            <div class="result-section">
                <h3>📰 新闻分析师</h3>
                <div class="agent-report"><pre id="newsReport">等待分析...</pre></div>
            </div>
            <div class="result-section">
                <h3>🔬 研究员辩论</h3>
                <div class="agent-report"><pre id="debateReport">等待分析...</pre></div>
            </div>
            <div class="result-section">
                <h3>⚠️ 风险评估</h3>
                <div class="agent-report"><pre id="riskReport">等待分析...</pre></div>
            </div>
        </div>
        <div class="footer">
            TradingAgents v1.0 · 数据来源: Yahoo Finance, StockTwits, Reddit, Polymarket, FRED<br>
            ⚠️ 仅供参考，不构成投资建议
        </div>
    </div>
    <script>
        function setTicker(t) {
            document.getElementById('ticker').value = t;
        }
        document.getElementById('analysisForm').addEventListener('submit', async function(e) {
            e.preventDefault();
            const ticker = document.getElementById('ticker').value.trim();
            const date = document.getElementById('date').value;
            if (!ticker) return;
            const btn = document.getElementById('submitBtn');
            const statusCard = document.getElementById('statusCard');
            const resultsCard = document.getElementById('resultsCard');
            btn.disabled = true;
            btn.textContent = '⏳ 分析中...';
            statusCard.classList.add('active');
            resultsCard.classList.remove('active');
            const logBox = document.getElementById('logBox');
            logBox.innerHTML = '<span class="info">⏳ 提交分析任务...</span><br>';
            try {
                const resp = await fetch('/api/analyze', {
                    method: 'POST',
                    headers: {'Content-Type': 'application/json'},
                    body: JSON.stringify({ticker, date})
                });
                const data = await resp.json();
                if (data.error) {
                    logBox.innerHTML += `<span class="error">❌ ${data.error}</span><br>`;
                    btn.disabled = false;
                    btn.textContent = '🚀 开始分析';
                    return;
                }
                const taskId = data.task_id;
                logBox.innerHTML += `<span class="info">📋 任务ID: ${taskId}</span><br>`;
                // Poll for results
                pollTask(taskId);
            } catch(err) {
                logBox.innerHTML += `<span class="error">❌ 请求失败: ${err.message}</span><br>`;
                btn.disabled = false;
                btn.textContent = '🚀 开始分析';
            }
        });
        async function pollTask(taskId) {
            const logBox = document.getElementById('logBox');
            const btn = document.getElementById('submitBtn');
            const statusCard = document.getElementById('statusCard');
            const resultsCard = document.getElementById('resultsCard');
            const maxPolls = 120;
            for (let i = 0; i < maxPolls; i++) {
                await new Promise(r => setTimeout(r, 5000));
                try {
                    const resp = await fetch(`/api/status/${taskId}`);
                    const data = await resp.json();
                    // Update logs
                    if (data.logs && data.logs.length > 0) {
                        logBox.innerHTML = data.logs.map(l => {
                            const cls = l.includes('✅') ? 'success' : l.includes('❌') ? 'error' : 'info';
                            return `<span class="${cls}">${l}</span>`;
                        }).join('<br>');
                        logBox.scrollTop = logBox.scrollHeight;
                    }
                    if (data.status === 'completed') {
                        btn.disabled = false;
                        btn.textContent = '🚀 开始分析';
                        // Show results
                        resultsCard.classList.add('active');
                        if (data.result) {
                            // Decision
                            const decision = data.result.final_decision || 'HOLD';
                            const decEl = document.getElementById('decisionValue');
                            decEl.textContent = decision;
                            decEl.className = 'decision-value ' + 
                                (decision.includes('BUY') ? 'decision-buy' : 
                                 decision.includes('SELL') ? 'decision-sell' : 'decision-hold');
                            // Reports
                            const reports = data.result.reports || {};
                            document.getElementById('techReport').textContent = reports.technical || '无数据';
                            document.getElementById('sentimentReport').textContent = reports.sentiment || '无数据';
                            document.getElementById('newsReport').textContent = reports.news || '无数据';
                            document.getElementById('debateReport').textContent = reports.debate || '无数据';
                            document.getElementById('riskReport').textContent = reports.risk || '无数据';
                        }
                        logBox.innerHTML += '<br><span class="success">✅ 分析完成！</span>';
                        return;
                    }
                    if (data.status === 'failed') {
                        btn.disabled = false;
                        btn.textContent = '🚀 开始分析';
                        logBox.innerHTML += `<br><span class="error">❌ 分析失败: ${data.error}</span>`;
                        return;
                    }
                } catch(err) {
                    logBox.innerHTML += `<br><span class="error">❌ 状态查询失败: ${err.message}</span>`;
                }
            }
            btn.disabled = false;
            btn.textContent = '🚀 开始分析';
            logBox.innerHTML += '<br><span class="error">❌ 分析超时</span>';
        }
    </script>
 </body>
 </html>
 """
@app.route('/')
 def index():
    default_date = datetime.now().strftime('%Y-%m-%d')
    return render_template_string(HTML_TEMPLATE, default_date=default_date)
@app.route('/api/analyze', methods=['POST'])
 def analyze():
    data = request.json
    ticker = data.get('ticker', '').strip()
    date = data.get('date', datetime.now().strftime('%Y-%m-%d'))
    if not ticker:
        return jsonify({'error': '请输入股票代码'}), 400
    task_id = str(uuid.uuid4())[:8]
    tasks[task_id] = {
        'status': 'running',
        'ticker': ticker,
        'date': date,
        'logs': ['⏳ 初始化分析...'],
        'result': None,
        'error': None,
        'started_at': datetime.now().isoformat()
    }
    # 在后台线程运行分析
    thread = threading.Thread(target=run_analysis, args=(task_id, ticker, date))
    thread.daemon = True
    thread.start()
    return jsonify({'task_id': task_id, 'status': 'running'})
@app.route('/api/status/<task_id>')
 def task_status(task_id):
    task = tasks.get(task_id)
    if not task:
        return jsonify({'error': '任务不存在'}), 404
    return jsonify(task)
 def run_analysis(task_id, ticker, date):
    """后台运行TradingAgents分析。"""
    import sys
    import io
    try:
        task = tasks[task_id]
        def log(msg):
            task['logs'].append(msg)
            # 保持日志不超过50条
            if len(task['logs']) > 50:
                task['logs'] = task['logs'][-50:]
        # 设置环境变量
        os.environ['OPENAI_COMPATIBLE_API_KEY'] = 'tp-ce6eaqg1gh1uev46i4oexh4sahloyfo2hfpxuc2n9m7yl9dd'
        os.environ['FRED_API_KEY'] = '825283926dd016ea66e96cc919e047da'
        os.environ['ALPHA_VANTAGE_API_KEY'] = 'W5PTA2J5OB16W02L'
        log(f'📊 开始分析 {ticker} ({date})')
        log(f'🤖 模型: MiMo-v2.5-pro')
        log(f'🌐 数据源: Alpha Vantage + SSH中转代理')
        from tradingagents.graph.trading_graph import TradingAgentsGraph
        from tradingagents.default_config import DEFAULT_CONFIG
        config = DEFAULT_CONFIG.copy()
        config['llm_provider'] = 'openai_compatible'
        config['backend_url'] = 'https://token-plan-cn.xiaomimimo.com/v1'
        config['deep_think_llm'] = 'mimo-v2.5-pro'
        config['quick_think_llm'] = 'mimo-v2.5-pro'
        config['max_debate_rounds'] = 1
        config['max_risk_discuss_rounds'] = 1
        # 配置数据源：Yahoo Finance优先，Alpha Vantage作为备选
        config['data_vendors'] = {
            'core_stock_apis': 'yfinance,alpha_vantage',
            'technical_indicators': 'yfinance,alpha_vantage',
            'fundamental_data': 'yfinance,alpha_vantage',
            'news_data': 'yfinance,alpha_vantage',
            'macro_data': 'fred',
            'prediction_markets': 'polymarket',
        }
        log('🔧 配置完成，启动分析引擎...')
        log('⏳ 分析通常需要3-8分钟，请耐心等待...')
        # 捕获stdout输出以获取实时进度
        class LogCapture:
            def __init__(self, task):
                self.task = task
                self.buffer = ''
            def write(self, text):
                self.buffer += text
                if '\n' in self.buffer:
                    lines = self.buffer.split('\n')
                    self.buffer = lines[-1]
                    for line in lines[:-1]:
                        line = line.strip()
                        if line and len(line) > 5:
                            # 只保留有意义的输出
                            if any(k in line for k in ['Tool', 'Message', 'Agent', 'failed', 'error', '✅', '❌', '📊', '📈', '💭', '📰', '🔬', '⚠️']):
                                self.task['logs'].append(f'📋 {line[:100]}')
                                if len(self.task['logs']) > 50:
                                    self.task['logs'] = self.task['logs'][-50:]
            def flush(self):
                pass
        # 临时捕获输出
        old_stdout = sys.stdout
        sys.stdout = LogCapture(task)
        try:
            ta = TradingAgentsGraph(debug=True, config=config)
            log('📈 技术分析师开始工作...')
            state, decision = ta.propagate(ticker, date)
        finally:
            sys.stdout = old_stdout
        log(f'✅ 分析完成: {decision}')
        # 提取各Agent的报告
        reports = {}
        # 从state中提取各阶段报告
        if 'market_report' in state:
            reports['technical'] = state['market_report'][:2000]
        if 'sentiment_report' in state:
            reports['sentiment'] = state['sentiment_report'][:2000]
        if 'news_report' in state:
            reports['news'] = state['news_report'][:2000]
        if 'investment_debate_state' in state:
            debate = state['investment_debate_state']
            if isinstance(debate, dict):
                reports['debate'] = str(debate.get('judge_decision', ''))[:2000]
            else:
                reports['debate'] = str(debate)[:2000]
        if 'risk_debate_state' in state:
            risk = state['risk_debate_state']
            if isinstance(risk, dict):
                reports['risk'] = str(risk.get('judge_decision', ''))[:2000]
            else:
                reports['risk'] = str(risk)[:2000]
        task['status'] = 'completed'
        task['result'] = {
            'final_decision': decision,
            'reports': reports
        }
        task['logs'].append(f'✅ 最终决策: {decision}')
    except Exception as e:
        task = tasks.get(task_id)
        if task:
            task['status'] = 'failed'
            task['error'] = str(e)
            task['logs'].append(f'❌ 错误: {str(e)}')
 if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8890, debug=False)
Author	SHA1	Message	Date
小钰	4e0f2c5e74	improve: 增强进度反馈，捕获TradingAgents实时输出 CI / tests (py3.10) (push) Has been cancelled Details CI / tests (py3.11) (push) Has been cancelled Details CI / tests (py3.12) (push) Has been cancelled Details CI / tests (py3.13) (push) Has been cancelled Details CI / clean-install smoke (push) Has been cancelled Details CI / ruff (strict, full repo) (push) Has been cancelled Details - 捕获stdout输出同步到前端日志 - 添加等待提示（3-8分钟） - 过滤有意义的进度信息	2026-07-08 17:36:39 +08:00
小钰	47c523852f	fix: 缓存加载后Date列转datetime，修复类型比较错误从CSV缓存加载时Date列是字符串，需要转为datetime才能与Timestamp比较	2026-07-08 17:21:33 +08:00
小钰	c05413d4fa	fix: Alpha Vantage使用免费端点TIME_SERIES_DAILY TIME_SERIES_DAILY_ADJUSTED是付费端点，改为免费的TIME_SERIES_DAILY	2026-07-08 17:15:16 +08:00
小钰	9a6c476ff1	feat: Alpha Vantage备选数据源配置 - Alpha Vantage API Key: W5PTA2J5OB16W02L - 数据源优先级: Yahoo Finance → Alpha Vantage - 当Yahoo Finance限流时自动fallback到Alpha Vantage	2026-07-08 17:05:13 +08:00
小钰	af1391d6ae	feat: Web可视化页面 + trading.zhaidk.xyz域名 - web_app.py: Flask可视化页面，支持股票代码输入、实时状态、结果展示 - systemd服务: tradingagents-web.service (端口8890) - Cloudflare Tunnel: trading.zhaidk.xyz → localhost:8890 - 预设按钮: AAPL/NVDA/茅台/平安/腾讯/BTC/SPY	2026-07-08 14:38:57 +08:00
小钰	709ea0fd78	feat: SSH连接池串行化 + Polymarket/数据源统一走连接池 - 新增ssh_proxy_pool.py: 全局单例连接池，队列串行化SSH请求 - stockstats_utils.py: _fetch_via_ssh改用连接池 - polymarket.py: 跳过本地直接走SSH连接池 - 消除并发SSH资源竞争问题	2026-07-08 14:34:41 +08:00
小钰	5207a2bf18	fix: Polymarket SSH代理增强错误处理 - 捕获subprocess超时 - 检查空响应 - 捕获JSON解析错误 - 更清晰的错误信息	2026-07-08 14:00:38 +08:00
小钰	6bba181307	fix: 优化Yahoo Finance跳过策略 + Polymarket超时 - yf_retry: 直接抛出YFRateLimitError，跳过本地重试 - Polymarket: 直连超时从30s降到5s，快速fallback到SSH - 基础数据(yf_retry调用)将优雅降级，不阻塞分析流程	2026-07-08 13:53:12 +08:00
小钰	f8a47cb473	feat: 全面SSH中转代理 + mimo-v2.5-pro支持 - Yahoo Finance: 跳过本地限流，直接走SSH中转 - StockTwits: 直连403时自动fallback到SSH中转 - Reddit: 网络不可达时自动fallback到SSH中转 - Polymarket: 网络不可达时自动fallback到SSH中转 - FRED: 配置API Key (825283926dd016ea66e96cc919e047da) - .env: 新增FRED_API_KEY和OPENAI_COMPATIBLE_API_KEY - test_xiaomi.py: 小米mimo-v2.5-pro测试脚本	2026-07-08 11:26:03 +08:00
小钰	51d057e2c9	feat: Yahoo Finance SSH proxy + Xiaomi MiMo LLM support CI / tests (py3.10) (push) Has been cancelled Details CI / tests (py3.11) (push) Has been cancelled Details CI / tests (py3.12) (push) Has been cancelled Details CI / tests (py3.13) (push) Has been cancelled Details CI / clean-install smoke (push) Has been cancelled Details CI / ruff (strict, full repo) (push) Has been cancelled Details - Add SSH proxy via foreign server for Yahoo Finance bypass - Add fallback in load_ohlcv and get_YFin_data_online - Add LLM JSON decode retry (3 attempts) in NormalizedChatOpenAI - Add yahoo_proxy.py utility module - Add test_smoke.py and patches/	2026-07-07 15:30:20 +08:00
Yijia-Xiao	01477f9afb	chore: release v0.3.1 - correctness/stability patch: look-ahead filter, router crash-safety, checkpoint identity, crypto sentiment sources, configurable retries, Bedrock API-key auth - adds Claude Sonnet 5 / Fable 5 support	2026-07-05 14:29:07 +00:00
Yijia-Xiao	0f70af2f31	feat(llm): add Claude Sonnet 5 and Fable 5 to the catalog - refresh the Anthropic lineup to the current GA set (Fable 5, Opus 4.8, Sonnet 5, Opus 4.7, Haiku 4.5) - extend the effort gate to single-number Claude 5 IDs (claude-sonnet-5, claude-fable-5) so their effort setting is honored	2026-07-05 14:29:07 +00:00
Yijia-Xiao	43bd32befa	feat(llm): support Bedrock API-key auth via AWS_BEARER_TOKEN_BEDROCK - pass the token to ChatBedrockConverse as api_key so langchain-aws prefers bearer auth and an ambient AWS_PROFILE can't override it; no AWS access keys required #1103	2026-07-05 14:29:07 +00:00
Yijia-Xiao	a102afa090	fix(dataflows): map crypto to StockTwits/Reddit sentiment symbols - crypto reached StockTwits as Yahoo's BTC-USD (404) instead of BTC.X, and Reddit searched the dashed pair that barely matches; both now resolve the base via a shared crypto_base() helper, restoring crypto sentiment - also fixes a StockTwits resilience test class that pytest never collected #1113	2026-07-05 14:29:07 +00:00
Yijia-Xiao	daf1da9c35	fix(graph): key checkpoints on graph shape and expose the LLM retry budget - checkpoint resume keyed only by ticker+date silently continued the old graph under a different analyst selection / depth / asset mode; fold a run signature into the thread id #1089 - add llm_max_retries + TRADINGAGENTS_LLM_MAX_RETRIES, forwarded to every provider when set (int-coerced, rejects negatives/booleans), so a 429 burst can't abort a run #1091	2026-07-05 14:29:07 +00:00
Yijia-Xiao	b47a828a4f	fix(graph): give the shared debate/risk routers a complete path_map - should_continue_debate (2 edges) and should_continue_risk_analysis (3 edges) each returned more targets than any one edge mapped; a fall-through under prompt/i18n/refactor drift crashed LangGraph mid-run - share a complete DEBATE_PATH_MAP / RISK_ANALYSIS_PATH_MAP across every edge #1088	2026-07-05 14:29:07 +00:00
Yijia-Xiao	622f99d28a	fix(analysts): align the news prompt with the get_news tool signature - prompt advertised get_news(query, ...) but the tool takes a ticker, so the model hallucinated free-text query calls - advertise get_news(ticker, start_date, end_date) #1116	2026-07-05 14:29:07 +00:00
Yijia-Xiao	3570f2e1e6	fix(dataflows): apply the Alpha Vantage fundamentals look-ahead filter - the payload is a JSON string, so the dict-only guard skipped filtering and future-dated reports leaked into historical runs, breaking the #475 guarantee - parse before filtering; non-JSON bodies and an unset curr_date pass through #1115	2026-07-05 14:29:06 +00:00
Yijia-Xiao	85946c2f60	chore: release v0.3.0 - CI gate, unified verified data-access contract, provider and data-vendor registry - env-over-CLI config precedence, current-generation model catalog - programmatic report output, plus sweep fixes for data and structured output	2026-06-22 02:05:07 +00:00