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Add Streamlit trading dashboard scaffolding
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‎environment.yml‎

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name: trading-dashboard
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channels:
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- defaults
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- conda-forge
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dependencies:
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- python>=3.10
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- streamlit>=1.32
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- pandas>=2.2
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- numpy>=1.26
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- httpx>=0.27
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- python-dotenv>=1.0
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- pip
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- pip:
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- futu-api>=7.2.4600
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- pydantic>=2.7
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- openai>=1.3
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- pytest>=7.4

‎pyproject.toml‎

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@@ -11,7 +11,10 @@ dependencies = [
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"pydantic>=2.7",
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"openai>=1.3",
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"httpx>=0.27",
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"python-dotenv>=1.0"
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"python-dotenv>=1.0",
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"streamlit>=1.32",
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"pandas>=2.2",
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"numpy>=1.26"
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]
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[project.optional-dependencies]

‎trading_dashboard/app.py‎

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"""Streamlit entry-point for the trading dashboard."""
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from __future__ import annotations
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import pandas as pd
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import streamlit as st
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from .executor import PaperTradeExecutor
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from .fetcher import MockMarketDataFetcher
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from .llm_client import LLMClient
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from .prompt_builder import PromptBuilder
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st.set_page_config(page_title="Trading Dashboard", layout="wide", page_icon="📈")
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def _init_state() -> None:
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if "fetcher" not in st.session_state:
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st.session_state.fetcher = MockMarketDataFetcher()
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if "executor" not in st.session_state:
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st.session_state.executor = PaperTradeExecutor()
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if "llm_client" not in st.session_state:
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st.session_state.llm_client = LLMClient()
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if "prompt_builder" not in st.session_state:
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st.session_state.prompt_builder = PromptBuilder()
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def _refresh_snapshot() -> None:
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fetcher: MockMarketDataFetcher = st.session_state.fetcher
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executor: PaperTradeExecutor = st.session_state.executor
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llm_client: LLMClient = st.session_state.llm_client
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prompt_builder: PromptBuilder = st.session_state.prompt_builder
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market = fetcher.fetch_latest()
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account = executor.get_account_state(market.price)
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prompt = prompt_builder.build_prompt(market, account)
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decision = llm_client.get_trading_decision(prompt)
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st.session_state.market = market
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st.session_state.account = account
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st.session_state.prompt = prompt
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st.session_state.decision = decision
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def _render_market_panel() -> None:
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market = st.session_state.market
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st.subheader("🔹 行情面板")
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cols = st.columns(5)
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cols[0].metric("价格", f"{market.price:.2f}")
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cols[1].metric("EMA(20)", f"{market.ema:.2f}")
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cols[2].metric("MACD", f"{market.macd:.4f}")
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cols[3].metric("MACD Signal", f"{market.macd_signal:.4f}")
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cols[4].metric("RSI", f"{market.rsi:.2f}")
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st.metric("成交量", f"{market.volume:,.0f}")
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history = market.history.copy()
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history.set_index("time", inplace=True)
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history.index = pd.to_datetime(history.index)
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st.line_chart(history["close"], height=240)
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def _render_account_panel() -> None:
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account = st.session_state.account
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st.subheader("🔹 账户面板")
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cols = st.columns(3)
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cols[0].metric("账户价值", f"{account.total_value:,.2f}")
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cols[1].metric("现金", f"{account.cash:,.2f}")
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positions = ", ".join(f"{symbol}: {qty}" for symbol, qty in account.positions.items()) or "无"
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cols[2].metric("持仓", positions)
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def _render_ai_panel() -> None:
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st.subheader("🔹 AI 决策展示")
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st.markdown(
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"当前策略建议："
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f"<span style='font-size:28px; font-weight:bold;'> {st.session_state.decision}</span>",
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unsafe_allow_html=True,
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)
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with st.expander("查看 Prompt"):
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st.code(st.session_state.prompt)
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def _render_order_panel() -> None:
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st.subheader("🔹 模拟下单")
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market = st.session_state.market
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executor: PaperTradeExecutor = st.session_state.executor
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action = st.selectbox(
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"选择操作",
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["Buy", "Sell", "Hold"],
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index=["Buy", "Sell", "Hold"].index(st.session_state.decision),
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)
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if st.button("执行下单"):
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result = executor.execute(action, market)
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st.success(f"{result.message} 数量: {result.quantity}, 价格: {result.price:.2f}")
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st.session_state.account = executor.get_account_state(market.price)
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if executor.order_history:
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st.markdown("**订单历史**")
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history_data = [
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{
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"时间": order.timestamp.strftime("%H:%M:%S"),
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"操作": order.action,
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"数量": order.quantity,
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"价格": f"{order.price:.2f}",
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"备注": order.message,
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}
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for order in reversed(executor.order_history[-10:])
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]
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st.table(history_data)
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def main() -> None:
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st.markdown("<meta http-equiv='refresh' content='180'>", unsafe_allow_html=True)
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st.title("🚀 AI Trading Dashboard")
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st.caption("最小可行闭环：实时行情 + 账户状态 + AI 策略 + 一键下单")
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_init_state()
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_refresh_snapshot()
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if st.button("手动刷新"):
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_refresh_snapshot()
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col1, col2 = st.columns([2, 1])
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with col1:
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_render_market_panel()
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with col2:
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_render_account_panel()
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col3, col4 = st.columns(2)
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with col3:
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_render_ai_panel()
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with col4:
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_render_order_panel()
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if __name__ == "__main__":
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main()

‎trading_dashboard/executor.py‎

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"""Paper trading execution utilities."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Dict, List
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from .fetcher import AccountState, MarketSnapshot
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@dataclass
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class OrderResult:
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action: str
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quantity: float
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price: float
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message: str
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timestamp: datetime
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@dataclass
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class PaperTradeExecutor:
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"""Minimal in-memory execution engine used by the dashboard."""
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symbol: str = "AAPL"
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lot_size: float = 1.0
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cash: float = 100_000.0
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positions: Dict[str, float] = field(default_factory=dict)
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order_history: List[OrderResult] = field(default_factory=list)
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def get_account_state(self, market_price: float) -> AccountState:
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position_qty = self.positions.get(self.symbol, 0.0)
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position_value = position_qty * market_price
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total_value = self.cash + position_value
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return AccountState(
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total_value=total_value,
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cash=self.cash,
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positions={self.symbol: position_qty},
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)
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def execute(self, action: str, market: MarketSnapshot) -> OrderResult:
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action = action.capitalize()
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if action not in {"Buy", "Sell", "Hold"}:
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raise ValueError(f"Unsupported action: {action}")
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qty = self.lot_size
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price = market.price
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position_qty = self.positions.get(self.symbol, 0.0)
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now = datetime.utcnow()
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if action == "Buy":
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cost = qty * price
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if cost > self.cash:
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result = OrderResult(action, 0.0, price, "Insufficient cash to buy.", now)
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else:
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self.cash -= cost
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self.positions[self.symbol] = position_qty + qty
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result = OrderResult(action, qty, price, "Filled buy order.", now)
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elif action == "Sell":
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if position_qty < qty:
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result = OrderResult(action, 0.0, price, "Not enough holdings to sell.", now)
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else:
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self.cash += qty * price
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self.positions[self.symbol] = position_qty - qty
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result = OrderResult(action, qty, price, "Filled sell order.", now)
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else: # Hold
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result = OrderResult(action, 0.0, price, "No trade executed.", now)
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self.order_history.append(result)
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return result
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__all__ = ["OrderResult", "PaperTradeExecutor"]

‎trading_dashboard/fetcher.py‎

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"""Data fetching utilities for the Streamlit trading dashboard.
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This module currently provides a mock implementation that simulates price
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movements so the dashboard can run end-to-end without external market data
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providers. The API has been designed so a real implementation can replace the
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mock with minimal changes.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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from typing import Dict
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import numpy as np
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import pandas as pd
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@dataclass
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class MarketSnapshot:
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"""Aggregated view of the latest market indicators."""
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symbol: str
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price: float
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ema: float
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macd: float
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macd_signal: float
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rsi: float
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volume: float
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history: pd.DataFrame
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@dataclass
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class AccountState:
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"""Basic representation of the account used in the dashboard."""
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total_value: float
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cash: float
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positions: Dict[str, float]
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class MockMarketDataFetcher:
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"""Simulate a live market data feed.
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The fetcher maintains an in-memory price series and generates technical
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indicators – EMA, MACD, RSI, and volume – each time ``fetch_latest`` is
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called. The historical data is kept in a pandas ``DataFrame`` to make chart
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rendering straightforward inside Streamlit.
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"""
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def __init__(self, symbol: str = "AAPL", history_points: int = 120) -> None:
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self.symbol = symbol
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self.history_points = history_points
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self._history = self._generate_initial_history()
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def _generate_initial_history(self) -> pd.DataFrame:
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end = datetime.utcnow()
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index = [end - timedelta(minutes=i) for i in reversed(range(self.history_points))]
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base_price = 150.0
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noise = np.random.normal(0, 0.5, size=self.history_points).cumsum()
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close = base_price + noise
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volume = np.random.randint(1_000, 10_000, size=self.history_points)
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return pd.DataFrame({"time": index, "close": close, "volume": volume})
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def _append_new_row(self) -> None:
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last_price = float(self._history.iloc[-1]["close"])
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drift = np.random.normal(0, 0.8)
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new_price = max(0.01, last_price + drift)
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new_row = {
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"time": datetime.utcnow(),
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"close": new_price,
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"volume": int(np.random.randint(1_000, 10_000)),
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}
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self._history = pd.concat([self._history.iloc[1:], pd.DataFrame([new_row])], ignore_index=True)
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def _compute_indicators(self, history: pd.DataFrame) -> Dict[str, float]:
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closes = history["close"]
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ema = closes.ewm(span=20, adjust=False).mean()
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ema12 = closes.ewm(span=12, adjust=False).mean()
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ema26 = closes.ewm(span=26, adjust=False).mean()
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macd = ema12 - ema26
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signal = macd.ewm(span=9, adjust=False).mean()
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delta = closes.diff()
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gain = delta.clip(lower=0)
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loss = -delta.clip(upper=0)
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avg_gain = gain.rolling(window=14).mean()
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avg_loss = loss.rolling(window=14).mean()
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rs = avg_gain / (avg_loss + 1e-9)
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rsi = 100 - (100 / (1 + rs))
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return {
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"ema": float(ema.iloc[-1]),
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"macd": float(macd.iloc[-1]),
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"signal": float(signal.iloc[-1]),
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"rsi": float(rsi.iloc[-1]),
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}
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def fetch_latest(self) -> MarketSnapshot:
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"""Return the newest snapshot and update internal state."""
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self._append_new_row()
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history = self._history.copy()
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indicators = self._compute_indicators(history)
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latest = history.iloc[-1]
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return MarketSnapshot(
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symbol=self.symbol,
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price=float(latest["close"]),
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ema=indicators["ema"],
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macd=indicators["macd"],
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macd_signal=indicators["signal"],
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rsi=indicators["rsi"],
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volume=float(latest["volume"]),
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history=history,
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)
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__all__ = [
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"AccountState",
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"MarketSnapshot",
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"MockMarketDataFetcher",
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]

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