qchen34--tradingagents
wehub-resource-sync
253 行
11 KiB
Python
253 行
11 KiB
Python
"""Step 3 — Entry Mode Decision: Price Distance Assessment.
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UX pattern:
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- Page load: show last cached results
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- "更新判断" button: fetch fresh data, compute, save, display
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- Chart: only after "更新判断" in current session
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"""
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from __future__ import annotations
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import matplotlib.pyplot as plt
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import pandas as pd
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import streamlit as st
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import yfinance as yf
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from web.pages.lrs import strategy_cache as sc
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from web.pages.lrs.strategy_shared import apply_dark_style, render_score_card
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_CACHE_KEY = "validation"
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_SKEY = "lrs_step3"
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@st.cache_data(ttl=300, show_spinner=False)
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def _fetch_data() -> tuple[pd.DataFrame, pd.DataFrame]:
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tqqq = yf.download("TQQQ", period="6mo", auto_adjust=True, progress=False)
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qqq = yf.download("QQQ", period="2y", auto_adjust=True, progress=False)
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for df in (tqqq, qqq):
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.get_level_values(0)
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return tqqq, qqq
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def _find_crossover(qqq_df: pd.DataFrame):
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close = qqq_df["Close"].dropna()
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ma200 = close.rolling(200).mean()
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above = (close > ma200).dropna()
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for i in range(len(above) - 1, 0, -1):
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if above.iloc[i] and not above.iloc[i - 1]:
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return above.index[i]
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return None
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def _entry_mode(distance_pct: float, macro_score: float) -> tuple[str, str, str]:
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if macro_score >= 5: threshold = 8.0
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elif macro_score >= 3: threshold = 6.0
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elif macro_score >= 2: threshold = 4.0
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else:
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return ("HOLD", "不入场",
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f"宏观评分 {macro_score:.0f}/5 过低，请返回 Step 1。")
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if distance_pct < 0:
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return ("Reassess", "返回 Step 2",
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"TQQQ 低于突破日价格 — 请重新确认 QQQ 是否仍在 MA200 之上。")
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if distance_pct <= 4.0:
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return ("Direct Buy", "2/3 直接买入 + 1/3 Wheel 保证金",
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f"距离 {distance_pct:.1f}% — 最佳入场，安全边际最大。")
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if distance_pct <= threshold:
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return ("Direct Buy", "2/3 直接买入 + 1/3 Wheel 保证金",
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f"距离 {distance_pct:.1f}% — 突破较新，直接买入合适。")
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if distance_pct <= 15.0:
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return ("Split", "1/3 直接买入 + 1/3 Sell Put + 1/3 预留",
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f"距离 {distance_pct:.1f}% — TQQQ 已有一定涨幅，分批入场降低追高风险。")
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return ("Split（强烈建议）", "1/3 直接买入 + 1/3 Sell Put + 1/3 预留",
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f"距离 {distance_pct:.1f}% — 已大幅上涨，历史上回调频率较高。")
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def _to_cache(cross_idx, cross_price, current_price, distance_pct,
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mode, mode_detail, rationale, macro_score) -> dict:
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return {
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"cross_idx": str(cross_idx.date()) if cross_idx is not None else None,
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"cross_price": float(cross_price) if cross_price is not None else None,
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"current_price": float(current_price),
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"distance_pct": float(distance_pct),
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"entry_mode": mode,
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"mode_detail": mode_detail,
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"rationale": rationale,
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"macro_score": float(macro_score),
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}
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def _render_distance_chart(tqqq_close, cross_idx, cross_price, current_price, save: bool = False) -> None:
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with st.container(border=True):
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st.markdown("**图表 — TQQQ 价格与突破基准距离**")
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fig, ax = plt.subplots(figsize=(12, 5))
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t90 = tqqq_close.tail(90)
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ax.plot(t90.index, t90.values, color="#38bdf8", linewidth=1.8, label="TQQQ Close")
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if cross_idx is not None and cross_price is not None:
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ax.axvline(x=cross_idx, color="#f59e0b", linestyle="--", linewidth=1.3,
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label=f"Crossover ({cross_idx.date()})")
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ax.axhline(y=cross_price, color="#a78bfa", linestyle="--", linewidth=1.2,
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label=f"Cross price ${cross_price:.2f}")
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ax.axhline(y=current_price, color="#22c55e", linestyle=":", linewidth=1.2,
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label=f"Current ${current_price:.2f}")
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fc = "#22c55e" if current_price >= cross_price else "#ef4444"
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ax.fill_between(t90.index, cross_price, current_price, alpha=0.12, color=fc)
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ax.set_title("TQQQ — Distance from MA200 Crossover Day"); ax.set_ylabel("Price (USD)")
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ax.legend(loc="upper left", fontsize=9)
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apply_dark_style(fig, ax)
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plt.tight_layout()
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if save:
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sc.save_figure("validation_chart", fig)
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st.pyplot(fig, clear_figure=True)
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def _render_all(data: dict, is_fresh: bool = False) -> None:
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cross_price = data.get("cross_price")
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current_price = data.get("current_price", 0.0)
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distance_pct = data.get("distance_pct", 0.0)
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cross_idx = data.get("cross_idx")
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macro_score = float(data.get("macro_score", 3))
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mode = data.get("entry_mode", "N/A")
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mode_detail = data.get("mode_detail", "")
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rationale = data.get("rationale", "")
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with st.container(border=True):
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st.markdown("#### 3.1 — 价格距离计算")
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st.caption("公式：Distance % = (TQQQ 当前价 - TQQQ 突破当日收盘价) / 突破当日收盘价 x 100")
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if cross_price is not None:
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col1, col2, col3 = st.columns(3)
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col1.metric("TQQQ 突破当日收盘价", f"${cross_price:.2f}",
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help=f"QQQ 突破日期：{cross_idx}")
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col2.metric("TQQQ 当前价格", f"${current_price:.2f}")
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col3.metric("距突破涨幅", f"{distance_pct:+.1f}%",
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delta=f"{distance_pct:+.1f}%")
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d_score = 1 if distance_pct <= 8 else (-1 if distance_pct <= 15 else 0)
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render_score_card(
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label="距离评估",
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score=d_score,
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metric=f"涨幅 = {distance_pct:+.1f}% | 突破日：{cross_idx} | 突破价：${cross_price:.2f} | 当前价：${current_price:.2f}",
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verdict=("突破时间较近 — 直接买入条件有利。"
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if distance_pct <= 8 else
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"已大幅上涨 — 建议使用分批入场模式。"),
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)
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st.session_state["lrs_step3_distance_pct"] = distance_pct
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st.session_state["lrs_step3_cross_price"] = cross_price
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st.session_state["lrs_step3_current_price"] = current_price
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else:
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st.warning("未检测到 MA200 看涨突破，请先完成 Step 2 分析。")
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distance_pct = 0.0
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if is_fresh:
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live = st.session_state.get(f"{_SKEY}_live_data")
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if live is not None:
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_render_distance_chart(live["tqqq_close"], live["cross_idx"],
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cross_price, current_price, save=True)
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else:
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img = sc.figure_path("validation_chart")
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if img:
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st.image(img, use_container_width=True)
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else:
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st.info("点击「更新判断」生成图表。")
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with st.container(border=True):
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st.markdown("#### 3.2 — 入场模式判断")
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st.caption(f"宏观评分（来自宏观经济分析 tab）：**{macro_score:.0f} / 5**")
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st.markdown("""
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| Distance | Macro >=5 | Macro 3-4 | Macro 2 | Entry Mode |
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|---|---|---|---|---|
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| 0% – 4% | Direct Buy | Direct Buy | Direct Buy | Optimal |
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| 4% – 8% | Direct Buy | Direct Buy | **Split** | Still good |
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| 8% – 15% | **Split** | **Split** | **Split** | Partial buy + Sell Put |
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| 15%+ | **Split** | **Split** | **Split** | Strongly recommended |
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""")
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_MODE_SCORE = {"Direct Buy": 1, "Split": -1,
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"Split (strongly recommended)": -1, "Reassess": 0, "HOLD": 0}
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render_score_card(label=f"入场模式：{mode}", score=_MODE_SCORE.get(mode, -1),
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metric=mode_detail, verdict=rationale)
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st.session_state["lrs_step3_entry_mode"] = mode
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with st.container(border=True):
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st.markdown("#### Step 3 输出记录")
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cp_str = f"${cross_price:.2f}" if cross_price else "N/A"
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st.markdown(f"""
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| Field | Value |
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|---|---|
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| TQQQ 突破当日收盘价 | **{cp_str}** |
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| TQQQ 当前价格 | **${current_price:.2f}** |
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| 距突破涨幅 | **{distance_pct:+.1f}%** |
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| 宏观评分（来自 Step 1） | **{macro_score:.0f} / 5** |
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| 入场模式 | **{mode}** |
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| 资金分配 | **{mode_detail}** |
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""")
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st.caption("以上结果已存入 session_state，Step 4 将自动读取。")
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def render_tab_validation() -> None:
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st.markdown("### Step 3 — 入场模式判断（价格距离评估）")
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cached = sc.load(_CACHE_KEY)
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col1, col2 = st.columns([4, 1])
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with col1:
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if cached:
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st.caption(f"上次更新：{cached.get('_updated_at', '—')} | 点击「更新判断」获取最新数据")
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else:
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st.caption("暂无历史数据，请点击「更新判断」开始分析")
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with col2:
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update = st.button("🔄 更新判断", key=f"{_SKEY}_update",
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type="primary", use_container_width=True)
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if update:
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st.session_state.pop(f"{_SKEY}_chart_ran", None)
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try:
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with st.spinner("正在获取 TQQQ / QQQ 数据…"):
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_fetch_data.clear()
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tqqq_df, qqq_df = _fetch_data()
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except Exception as exc:
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st.error(f"数据获取失败：{exc}")
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return
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tqqq_close = tqqq_df["Close"].dropna() if not tqqq_df.empty else pd.Series(dtype=float)
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if tqqq_close.empty:
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st.error("TQQQ 数据下载失败（yfinance 返回空数据），请稍后重试。")
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return
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current_price = float(tqqq_close.iloc[-1])
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qqq_close_chk = qqq_df["Close"].dropna() if not qqq_df.empty else pd.Series(dtype=float)
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if qqq_close_chk.empty:
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st.error("QQQ 数据下载失败（yfinance 返回空数据），请稍后重试。")
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return
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cross_idx = _find_crossover(qqq_df)
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cross_price = None
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if cross_idx is not None:
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nearest = tqqq_close.index[tqqq_close.index >= cross_idx]
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if len(nearest) > 0:
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cross_price = float(tqqq_close.loc[nearest[0]])
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distance_pct = ((current_price - cross_price) / cross_price * 100
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if cross_price is not None else 0.0)
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macro_score = float(st.session_state.get("macro_score_total", 3))
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mode, mode_detail, rationale = _entry_mode(distance_pct, macro_score)
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sc.save(_CACHE_KEY, _to_cache(cross_idx, cross_price, current_price,
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distance_pct, mode, mode_detail, rationale, macro_score))
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st.session_state[f"{_SKEY}_live_data"] = {
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"tqqq_close": tqqq_close, "cross_idx": cross_idx,
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}
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st.success(f"数据已更新 — Entry Mode: **{mode}**")
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live = st.session_state.get(f"{_SKEY}_live_data")
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if live is not None:
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display_data = sc.load(_CACHE_KEY) or {}
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is_fresh = True
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elif cached:
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display_data = cached
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is_fresh = False
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st.session_state["lrs_step3_entry_mode"] = cached.get("entry_mode", "")
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st.session_state["lrs_step3_distance_pct"] = cached.get("distance_pct")
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st.session_state["lrs_step3_current_price"] = cached.get("current_price")
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else:
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st.info("点击「更新判断」开始分析。")
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return
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_render_all(display_data, is_fresh=is_fresh)
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