TradingAgents/ui/streamlit_app.py

228 lines
10 KiB
Python

# -*- coding: utf-8 -*-
"""
TradingAgents Streamlit UI
Run from project root:
pip install streamlit
streamlit run ui/streamlit_app.py
This UI wraps the same pipeline as the CLI (python -m cli.main analyze).
No business logic is duplicated: the UI builds a selections dict and calls
cli.main.run_analysis_programmatic via ui.cli_wrapper.
How CLI and UI share logic:
- Both use tradingagents.graph.TradingAgentsGraph and cli.main.save_report_to_disk.
- CLI: interactive prompts → run_analysis() with Rich live display.
- UI: form inputs → run_trading_agent() → run_analysis_programmatic() with log_callback.
Adding new agents: extend the graph and config, then add the analyst option
to the sidebar "Analyst / strategy selection" and to cli.models.AnalystType.
"""
from __future__ import annotations
import io
from pathlib import Path
from datetime import datetime, date
from typing import List, Optional
import streamlit as st
# Ensure project root is on path
_UI_DIR = Path(__file__).resolve().parent
_PROJECT_ROOT = _UI_DIR.parent
if str(_PROJECT_ROOT) not in __import__("sys").path:
__import__("sys").path.insert(0, str(_PROJECT_ROOT))
from ui import cli_wrapper
# -----------------------------------------------------------------------------
# Option constants (mirror CLI choices; no business logic)
# -----------------------------------------------------------------------------
LLM_PROVIDERS = [
("OpenAI", "openai", "https://api.openai.com/v1"),
("Ark (ByteDance)", "ark", "https://ark.ap-southeast.bytepluses.com/api/v3"),
("Google", "google", "https://generativelanguage.googleapis.com/v1"),
("Anthropic", "anthropic", "https://api.anthropic.com/"),
("xAI", "xai", "https://api.x.ai/v1"),
("Openrouter", "openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "ollama", "http://localhost:11434/v1"),
]
ANALYST_OPTIONS = [
("Market", "market"),
("Social Media", "social"),
("News", "news"),
("Fundamentals", "fundamentals"),
]
RESEARCH_DEPTH_OPTIONS = [
("Shallow — quick research, few rounds", 1),
("Medium — moderate debate rounds", 3),
("Deep — comprehensive research", 5),
]
# Per-provider model options (display, value)
SHALLOW_OPTIONS = {
"openai": [("GPT-5 Mini", "gpt-5-mini"), ("GPT-5 Nano", "gpt-5-nano"), ("GPT-5.2", "gpt-5.2"), ("GPT-4.1", "gpt-4.1")],
"anthropic": [("Claude Haiku 4.5", "claude-haiku-4-5"), ("Claude Sonnet 4.5", "claude-sonnet-4-5"), ("Claude Sonnet 4", "claude-sonnet-4-20250514")],
"google": [("Gemini 3 Flash", "gemini-3-flash-preview"), ("Gemini 2.5 Flash", "gemini-2.5-flash"), ("Gemini 2.5 Flash Lite", "gemini-2.5-flash-lite")],
"xai": [("Grok 4.1 Fast (Non-Reasoning)", "grok-4-1-fast-non-reasoning"), ("Grok 4 Fast (Reasoning)", "grok-4-fast-reasoning")],
"openrouter": [("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"), ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free")],
"ollama": [("Qwen3:latest", "qwen3:latest"), ("GPT-OSS:latest", "gpt-oss:latest"), ("GLM-4.7-Flash:latest", "glm-4.7-flash:latest")],
"ark": [("Ark seed-1-8-251228", "seed-1-8-251228")],
}
DEEP_OPTIONS = {
"openai": [("GPT-5.2", "gpt-5.2"), ("GPT-5.1", "gpt-5.1"), ("GPT-5", "gpt-5"), ("GPT-4.1", "gpt-4.1"), ("GPT-5 Mini", "gpt-5-mini")],
"anthropic": [("Claude Sonnet 4.5", "claude-sonnet-4-5"), ("Claude Opus 4.5", "claude-opus-4-5"), ("Claude Haiku 4.5", "claude-haiku-4-5")],
"google": [("Gemini 3 Pro", "gemini-3-pro-preview"), ("Gemini 3 Flash", "gemini-3-flash-preview"), ("Gemini 2.5 Flash", "gemini-2.5-flash")],
"xai": [("Grok 4.1 Fast (Reasoning)", "grok-4-1-fast-reasoning"), ("Grok 4 Fast (Reasoning)", "grok-4-fast-reasoning"), ("Grok 4", "grok-4-0709")],
"openrouter": [("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"), ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free")],
"ollama": [("GLM-4.7-Flash:latest", "glm-4.7-flash:latest"), ("GPT-OSS:latest", "gpt-oss:latest"), ("Qwen3:latest", "qwen3:latest")],
"ark": [("Ark seed-1-8-251228", "seed-1-8-251228")],
}
def _default_provider_options(provider_key: str):
shallow = SHALLOW_OPTIONS.get(provider_key, [("Default", "gpt-5-mini")])
deep = DEEP_OPTIONS.get(provider_key, [("Default", "gpt-5.2")])
return shallow, deep
def main() -> None:
st.set_page_config(
page_title="TradingAgents",
page_icon="📈",
layout="wide",
initial_sidebar_state="expanded",
)
# Minimal custom style for a clean, professional look
st.markdown("""
<style>
.stApp { max-width: 1400px; margin: 0 auto; }
.block-container { padding-top: 1.5rem; padding-bottom: 2rem; }
div[data-testid="stVerticalBlock"] > div:has(> div[data-testid="stMarkdown"]) { margin-bottom: 0.5rem; }
.report-preview { font-size: 0.9rem; line-height: 1.5; }
</style>
""", unsafe_allow_html=True)
# ----- Sidebar -----
with st.sidebar:
st.markdown("## 📊 TradingAgents")
st.markdown("---")
st.markdown("### Agent / strategy selection")
selected_analysts: List[str] = st.multiselect(
"Analyst team",
options=[v for _, v in ANALYST_OPTIONS],
default=["market", "news", "fundamentals"],
format_func=lambda x: next(d for d, v in ANALYST_OPTIONS if v == x),
)
if not selected_analysts:
st.warning("Select at least one analyst.")
st.markdown("### Symbols")
ticker_input = st.text_input("Ticker symbol(s)", value="SPY", help="Primary symbol; multi-symbol support can be extended.")
ticker = (ticker_input or "SPY").strip().upper().split()[0]
st.markdown("### Date range")
today = date.today()
analysis_date = st.date_input("Analysis date", value=today, max_value=today)
analysis_date_str = analysis_date.strftime("%Y-%m-%d")
st.markdown("### Capital / risk (optional)")
capital = st.number_input("Capital (reserved)", min_value=0.0, value=100000.0, step=10000.0, format="%.0f")
risk_pct = st.slider("Risk % (reserved)", 0.0, 50.0, 2.0, 0.5)
st.markdown("### Optional CLI flags")
research_depth_label, research_depth = st.selectbox(
"Research depth",
options=RESEARCH_DEPTH_OPTIONS,
index=1,
format_func=lambda x: x[0],
)
research_depth_value = research_depth
provider_display, provider_key, backend_url = st.selectbox(
"LLM provider",
options=LLM_PROVIDERS,
index=0,
format_func=lambda x: x[0],
)
shallow_opts, deep_opts = _default_provider_options(provider_key)
shallow_thinker = st.selectbox("Quick-thinking model", options=[v for _, v in shallow_opts], format_func=lambda x: next(d for d, v in shallow_opts if v == x))
deep_thinker = st.selectbox("Deep-thinking model", options=[v for _, v in deep_opts], format_func=lambda x: next(d for d, v in deep_opts if v == x))
google_thinking = None
openai_effort = None
if provider_key == "google":
google_thinking = st.selectbox("Gemini thinking mode", ["high", "minimal"], index=0)
elif provider_key == "openai":
openai_effort = st.selectbox("OpenAI reasoning effort", ["medium", "high", "low"], index=0)
st.markdown("---")
# ----- Main area -----
st.title("TradingAgents")
st.caption("Multi-Agents LLM Financial Trading — same pipeline as CLI, no logic duplication.")
run_clicked = st.button("Run Trading Agent", type="primary", use_container_width=True)
log_placeholder = st.empty()
report_placeholder = st.empty()
download_placeholder = st.empty()
error_placeholder = st.empty()
# Clear previous result when starting a new run
if run_clicked:
error_placeholder.empty()
download_placeholder.empty()
report_placeholder.empty()
log_lines: List[str] = []
def on_log(line: str) -> None:
log_lines.append(line)
with st.spinner("Running pipeline…"):
selections = {
"ticker": ticker,
"analysis_date": analysis_date_str,
"analysts": selected_analysts if selected_analysts else ["market", "news", "fundamentals"],
"research_depth": research_depth_value,
"llm_provider": provider_key,
"backend_url": backend_url,
"shallow_thinker": shallow_thinker,
"deep_thinker": deep_thinker,
"google_thinking_level": google_thinking,
"openai_reasoning_effort": openai_effort,
}
success, report_path, err_msg, final_state = cli_wrapper.run_trading_agent(selections, log_callback=on_log)
with log_placeholder:
st.markdown("#### Live execution log")
st.text_area("Log", value="\n".join(log_lines), height=280, key="run_log", label_visibility="collapsed")
if not success:
error_placeholder.error(f"Run failed: {err_msg}")
else:
st.success("Run completed. Report saved.")
preview_md = cli_wrapper.build_report_preview_markdown(final_state, ticker)
with report_placeholder:
st.markdown("### Report preview")
if preview_md:
st.markdown(preview_md, unsafe_allow_html=False)
else:
st.info("No preview content.")
if report_path and report_path.exists():
report_bytes = report_path.read_text(encoding="utf-8")
download_placeholder.download_button(
"Download report (complete_report.md)",
data=report_bytes,
file_name=report_path.name,
mime="text/markdown",
use_container_width=True,
)
with st.sidebar:
st.markdown("---")
st.markdown("**Docs**")
st.markdown("- CLI: `python -m cli.main analyze`")
st.markdown("- UI: `streamlit run ui/streamlit_app.py`")
if __name__ == "__main__":
main()