feat(config): expose sampling temperature and document reproducibility

Adds a cross-provider temperature config (and TRADINGAGENTS_TEMPERATURE),
forwarded to every LLM client when set, so runs can be made less variable
on models that honor it. Adds a README "Reproducibility" section that
separates the sources of run-to-run variation, what users can control
(temperature, non-reasoning model, pinned date), and what is inherent to
LLM-driven analysis, and notes that the identity and verified-data fixes
already removed the "different companies / fabricated prices" variance.

#178 #168

README.md

@@ -253,6 +253,28 @@ ta = TradingAgentsGraph(config=config)
 _, decision = ta.propagate("NVDA", "2026-01-15")
 ```
 
+## Reproducibility
+
+TradingAgents is LLM-driven, so two runs of the same ticker and date can differ. This is expected for a research tool built on language models, not a defect. The variation comes from a few distinct sources, and it helps to separate them.
+
+Language model sampling is non-deterministic. Even at a fixed temperature, providers do not guarantee byte-identical output across calls, and reasoning models (the default GPT-5.x family, and any thinking-mode model) vary the most because their internal reasoning is itself sampled.
+
+Live data moves. News, StockTwits, and Reddit return different content as time passes, so a run today sees different inputs than a run last week even for the same historical trade date. Pin the analysis date to hold the price and indicator window fixed, but the social and news sources still reflect "now".
+
+To reduce variation you can lower the sampling temperature. Set `temperature` in your config (or `TRADINGAGENTS_TEMPERATURE` in `.env`); lower values make models that honor it more repeatable. Reasoning models largely ignore temperature, so for tighter reproducibility pair a low temperature with a non-reasoning model such as `gpt-4.1`.
+
+```python
+config = DEFAULT_CONFIG.copy()
+config["llm_provider"] = "openai"
+config["deep_think_llm"] = "gpt-4.1"      # non-reasoning model honors temperature
+config["quick_think_llm"] = "gpt-4.1"
+config["temperature"] = 0.0
+```
+
+What does not vary anymore: the analyzed company identity is resolved deterministically from the ticker before any agent runs, and the market analyst grounds exact price and indicator claims in a verified data snapshot. Earlier reports of "different companies" or fabricated price levels across runs are addressed by these two mechanisms.
+
+Backtest results are not guaranteed to match any published figure. Returns depend on the model, the temperature, the date range, data quality, and the sampling above. Treat the framework as a research scaffold for studying multi-agent analysis, not as a strategy with a fixed, replicable return.
+
 ## Contributing
 
 We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).