Agents had no ground-truth ticker→company mapping, so the market analyst
could pattern-match a price chart to the wrong company (e.g. TOTDY read as
"TotalEnergies"), and every downstream agent inherited the bad framing.
Resolve identity once at run start via a cached, fail-open yfinance lookup
and inject company/sector/exchange into the shared instrument context that
all twelve agents consume, with an explicit do-not-substitute instruction.
Resolution runs on both the propagate() and CLI entry points.
Also replaces the bare "Continue" message-clear placeholder, which some
OpenAI-compatible providers interpreted as the user task, with a
context-anchored placeholder carrying the resolved identity and date.
#814#888
- analyst_execution.py: rename "Social Analyst" / "Msg Clear Social"
to "Sentiment Analyst" / "Msg Clear Sentiment" to match v0.2.5.
- conditional_logic.should_continue_social returns the renamed route.
- TradingAgentsGraph.propagate accepts asset_type and threads through
to Propagator.create_initial_state.
- Regression test on the Sentiment Analyst label.
Verified end-to-end (NVDA stock + BTC-USD crypto) on gpt-5.4-mini.
SPY was hardcoded as the alpha benchmark in both the return-fetch
path and the reflection label, which produced meaningless alpha for
.NS / .T / .HK / .L / .TO / .AX / .BO listings — FX drift between a
local-currency stock and a USD index dominates the spread.
DEFAULT_CONFIG now exposes benchmark_ticker (explicit override) and
benchmark_map (suffix → regional index, with SPY as the empty-suffix
default). TRADINGAGENTS_BENCHMARK_TICKER joins the env-overlay table.
Trading graph resolves the benchmark once per ticker and threads it
through to both _fetch_returns and reflect_on_final_decision, so the
alpha label reads "Alpha vs ^N225" for Tokyo listings, "Alpha vs ^HSI"
for Hong Kong, etc., instead of the misleading "Alpha vs SPY".
Pre-fetches news + StockTwits + Reddit via no-auth public endpoints
and injects structured data blocks into the prompt with professional
analysis instructions. Replaces the prompt-vs-tool mismatch that
caused fabricated social-platform content. Backward-compat alias +
"social" CLI key preserved.
#557#607
- dataflows/config: deepcopy + one-level dict merge so a partial
set_config doesn't clobber sibling defaults
- graph: thread max_recur_limit from config to Propagator
- openai_client: name the missing env var in the API-key error
#788#764#680
graph.stream() yields per-node deltas, not the full state. Taking
trace[-1] only captured the last node's contribution, so reports
saved to disk were missing every section except the final decision.
Merge all chunks in both the CLI path and trading_graph._run_graph's
debug branch.
#719#736
The ticker symbol reaches three filesystem-path construction sites
(load_ohlcv cache filename, checkpointer DB path, _log_state results
directory) without validation. A value containing path separators or
"../" escapes the configured cache / checkpoints / results directory.
Two attack vectors:
- Programmatic callers passing arbitrary ticker to propagate()
- Prompt injection via fetched news content steering the LLM into
tool calls with attacker-chosen ticker
Fix: new safe_ticker_component() validator in tradingagents/dataflows/
utils.py applied at all three sites. Allows the standard ticker
character set ([A-Za-z0-9._\-\^], up to 32 chars) and explicitly
rejects dot-only values like "." and ".." which would otherwise pass
the regex but traverse parent directories. Seven test cases cover
the accepted formats (BRK-B, 7203.T, ^GSPC, etc.) and the rejected
inputs (path separators, null bytes, whitespace, empty values,
overlong strings, dot-only values).
Closes#618.
Three related changes that take the rating pipeline from heuristic-only
to type-safe at the source.
1) Research Manager prompt now uses the same 5-tier scale (Buy /
Overweight / Hold / Underweight / Sell) as the Portfolio Manager,
signal_processing, and the memory log. The prior 3-tier wording
(Buy / Sell / Hold) was the only remaining inconsistency in the
pipeline.
2) Centralise the 5-tier vocabulary and the heuristic prose-rating
parser into tradingagents/agents/utils/rating.py. Both the memory
log and the signal processor now share the same parser instead of
duplicating regex and word-walker logic.
3) Make structured output a first-class part of the Portfolio Manager's
primary call. The PM uses llm.with_structured_output(PortfolioDecision)
so each provider's native structured-output mode (json_schema for
OpenAI/xAI, response_schema for Gemini, tool-use for Anthropic,
function_calling for OpenAI-compatible providers) yields a typed
Pydantic instance directly. A render helper turns that instance back
into the same markdown shape downstream consumers (memory log, CLI
display, saved reports) already expect, so no other code has to know
the PM now produces structured output. Providers without structured
support fall back gracefully to free-text + the deterministic
heuristic.
The previous SignalProcessor had been making a second LLM call to
re-extract the rating from the PM's prose; that round-trip is now
eliminated. SignalProcessor is a thin adapter over parse_rating(),
makes zero LLM calls, and stays for backwards compatibility with
process_signal() callers.
Schema (PortfolioDecision) captures rating + executive_summary +
investment_thesis + optional price_target + time_horizon, with field
descriptions doubling as output instructions. Agent prose remains the
primary artifact; structured output is layered onto the PM only because
it is the one agent whose output has machine-readable downstream
consumers.
15 new tests cover the heuristic parser (markdown-bold edge cases that
had no coverage before), the structured PM happy path, the free-text
fallback path, and that SignalProcessor never invokes the LLM. Full
suite: 77 tests pass in ~2s without API keys.
Long analyses can take many minutes; a crash or interruption forced users
to re-run from scratch and re-pay every LLM call. This adds an opt-in
checkpoint layer backed by per-ticker SQLite databases so the graph
resumes from the last successful node.
How to use:
- CLI: tradingagents analyze --checkpoint
- CLI: tradingagents analyze --clear-checkpoints
- Python: config["checkpoint_enabled"] = True
Lifecycle:
- propagate() recompiles the graph with a SqliteSaver when enabled and
injects a deterministic thread_id derived from ticker+date so the
same ticker+date resumes while a different date starts fresh.
- On successful completion the per-thread checkpoint rows are cleared.
- The context manager is closed in a try/finally so a crash never
leaks the SQLite connection or leaves the graph in checkpoint mode.
Storage: ~/.tradingagents/cache/checkpoints/<TICKER>.db
(override via TRADINGAGENTS_CACHE_DIR).
The checkpointer module is new (tradingagents/graph/checkpointer.py)
and the GraphSetup now returns the uncompiled workflow so it can be
recompiled with a saver when needed.
Adds langgraph-checkpoint-sqlite>=2.0.0 dependency. 3 new tests verify
the crash/resume cycle and that a different date starts fresh.
The previous per-agent BM25 memory was effectively dead code — its only
caller was a commented-out line in main.py. Replace it with a single
append-only markdown decision log driven by the propagate() lifecycle.
Lifecycle:
- store_decision() appends a pending entry at the end of every run
- _resolve_pending_entries() runs at the start of the next same-ticker
run, fetches yfinance returns + alpha vs SPY, and writes one LLM
reflection per resolved entry through an atomic temp-file rename
- Portfolio Manager consumes state["past_context"] (5 most recent
same-ticker entries plus 3 cross-ticker reflection-only excerpts)
Storage at ~/.tradingagents/memory/trading_memory.md
(override: TRADINGAGENTS_MEMORY_LOG_PATH).
Tag schema:
- Pending: [YYYY-MM-DD | TICKER | Rating | pending]
- Resolved: [YYYY-MM-DD | TICKER | Rating | +X.X% | +Y.Y% | Nd]
Removes rank-bm25 dependency and the legacy reflect_and_remember()
plumbing across reflection.py, trading_graph.py, and the agent factories.
49 new tests in tests/test_memory_log.py cover the storage, deferred
reflection, prompt injection, and legacy-removal paths. Full suite
(58 tests) passes in under 2 seconds without API keys.
Add effort parameter (high/medium/low) for Claude 4.5+ and 4.6 models,
consistent with OpenAI reasoning_effort and Google thinking_level.
Also add content normalization for Anthropic responses.
InvestDebateState was missing bull_history, bear_history, judge_decision.
RiskDebateState was missing aggressive_history, conservative_history,
neutral_history, latest_speaker, judge_decision. This caused KeyError
in _log_state() and reflection, especially with edge-case config values.
Prevents UnicodeEncodeError on Windows where the default encoding
(cp1252/gbk) cannot handle Unicode characters in LLM output.
Closes#77, closes#114, closes#126, closes#215, closes#332
- Add StatsCallbackHandler for tracking LLM calls, tool calls, and tokens
- Integrate callbacks into TradingAgentsGraph and all LLM clients
- Dynamic agent/report counts based on selected analysts
- Fix report completion counting (tied to agent completion)
Yijia-Xiao