qchen34--tradingagents
wehub-resource-sync
109 行
4.1 KiB
Python
109 行
4.1 KiB
Python
from __future__ import annotations
|
||
|
||
import streamlit as st
|
||
|
||
from web.services.dashboard_llm import build_llm_config
|
||
from web.services.strategy_api_service import answer_lrs_question, get_lrs_doc_markdown
|
||
from web.pages.lrs.strategy_macro import render_tab_macro
|
||
from web.pages.lrs.strategy_base_signal import render_tab_base_signal
|
||
from web.pages.lrs.strategy_validation import render_tab_validation
|
||
from web.pages.lrs.strategy_execution import render_tab_execution
|
||
from web.pages.lrs.strategy_risk import render_tab_risk
|
||
from web.pages.backtesting import render_backtest_tab
|
||
from web.pages.wheel.strategy_wheel import render_wheel_page
|
||
|
||
def _llm_answer(prompt: str) -> str:
|
||
cfg = build_llm_config(st.session_state.get("last_params"))
|
||
return answer_lrs_question(prompt, cfg)
|
||
|
||
|
||
def _render_lrs_dashboard() -> None:
|
||
st.subheader("LRS TQQQ 策略仪表盘")
|
||
st.caption('基于“当日开仓决策”框架。当前采用子 Tab 架构，优先展示宏观层。')
|
||
|
||
k1, k2, k3, k4 = st.columns(4)
|
||
k1.metric("策略模式", "LRS")
|
||
k2.metric("标的", "TQQQ")
|
||
k3.metric("决策状态", "待运行")
|
||
k4.metric("建议仓位", "N/A")
|
||
|
||
st.markdown("---")
|
||
|
||
tab_macro, tab_base, tab_valid, tab_risk, tab_exec = st.tabs([
|
||
"1) 宏观经济分析（优先）",
|
||
"2) 基础信号",
|
||
"3) 入场模式判断",
|
||
"4) 风控层（资金分配）",
|
||
"5) 执行层（下单）",
|
||
])
|
||
|
||
with tab_macro:
|
||
render_tab_macro()
|
||
|
||
with tab_base:
|
||
render_tab_base_signal()
|
||
|
||
with tab_valid:
|
||
render_tab_validation()
|
||
|
||
with tab_risk:
|
||
render_tab_risk()
|
||
|
||
with tab_exec:
|
||
render_tab_execution()
|
||
|
||
|
||
def render_strategy() -> None:
|
||
st.header("策略")
|
||
sub_strategy = str(st.session_state.get("strategy_sub_menu", "LRS TQQQ策略"))
|
||
st.caption(f"当前子策略：{sub_strategy}")
|
||
|
||
if sub_strategy == "Wheel策略":
|
||
render_wheel_page()
|
||
return
|
||
if sub_strategy != "LRS TQQQ策略":
|
||
st.info("未找到该子策略。")
|
||
return
|
||
|
||
tab_doc, tab_dashboard, tab_backtest = st.tabs(["策略文档", "策略仪表盘", "策略回测"])
|
||
|
||
with tab_doc:
|
||
left, right = st.columns([1.45, 1], gap="large")
|
||
with left:
|
||
st.markdown(get_lrs_doc_markdown())
|
||
with right:
|
||
st.markdown("#### LRS 策略问答（LLM）")
|
||
st.caption("可针对 LRS 思路、参数与风控方案进行讨论。")
|
||
chat_key = "lrs_chat_messages"
|
||
if chat_key not in st.session_state:
|
||
st.session_state[chat_key] = [{
|
||
"role": "assistant",
|
||
"content": "你好，我是 LRS 策略助手。你可以问我：如何定义入场条件、如何控制回撤、如何设计回测指标。",
|
||
}]
|
||
for msg in st.session_state[chat_key]:
|
||
with st.chat_message(msg["role"]):
|
||
st.markdown(msg["content"])
|
||
user_text = st.chat_input("输入你的策略问题...", key="lrs_chat_input")
|
||
if user_text:
|
||
st.session_state[chat_key].append({"role": "user", "content": user_text})
|
||
with st.chat_message("user"):
|
||
st.markdown(user_text)
|
||
with st.chat_message("assistant"):
|
||
with st.spinner("正在生成回答..."):
|
||
try:
|
||
answer = _llm_answer(
|
||
f"你是量化策略研究助手。请围绕 LRS TQQQ 策略回答问题，要求中文、结构化、可执行。\n\n用户问题：{user_text}"
|
||
).strip()
|
||
if not answer:
|
||
answer = "我暂时没有生成有效内容，请换个问法再试。"
|
||
except Exception as exc:
|
||
answer = f"LLM 调用失败：{exc}"
|
||
st.markdown(answer)
|
||
st.session_state[chat_key].append({"role": "assistant", "content": answer})
|
||
|
||
with tab_dashboard:
|
||
_render_lrs_dashboard()
|
||
|
||
with tab_backtest:
|
||
render_backtest_tab()
|