"""
Signals page — today's recommendation cards with rich visual indicators.
Each signal is displayed as a data-dense card with strategy badges,
confidence bars, and expandable thesis sections.
"""
from datetime import datetime
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from tradingagents.ui.theme import COLORS, get_plotly_template, page_header, signal_card
from tradingagents.ui.utils import load_recommendations
TIMEFRAME_LOOKBACK_DAYS = {
"7D": 7,
"1M": 30,
"3M": 90,
"6M": 180,
"1Y": 365,
}
@st.cache_data(ttl=3600)
def _load_price_history(ticker: str, period: str) -> pd.DataFrame:
try:
from tradingagents.dataflows.y_finance import download_history
except Exception:
return pd.DataFrame()
data = download_history(
ticker,
period=period,
interval="1d",
auto_adjust=True,
progress=False,
)
if data is None or data.empty:
return pd.DataFrame()
if isinstance(data.columns, pd.MultiIndex):
tickers = data.columns.get_level_values(1).unique()
target = ticker if ticker in tickers else tickers[0]
data = data.xs(target, level=1, axis=1).copy()
data = data.reset_index()
date_col = "Date" if "Date" in data.columns else data.columns[0]
close_col = "Close" if "Close" in data.columns else "Adj Close"
if close_col not in data.columns:
return pd.DataFrame()
history = data[[date_col, close_col]].rename(columns={date_col: "date", close_col: "close"})
history["date"] = pd.to_datetime(history["date"])
history = history.dropna(subset=["close"]).sort_values("date")
return history
def _slice_history_window(history: pd.DataFrame, timeframe: str) -> pd.DataFrame:
days = TIMEFRAME_LOOKBACK_DAYS.get(timeframe)
if history.empty or days is None:
return pd.DataFrame()
latest_date = history["date"].max()
cutoff = latest_date - pd.Timedelta(days=days)
window = history[history["date"] >= cutoff].copy()
if len(window) < 2:
return pd.DataFrame()
return window
def _format_move_pct(window: pd.DataFrame) -> str:
first_close = float(window["close"].iloc[0])
last_close = float(window["close"].iloc[-1])
if first_close == 0:
return "0.00%"
move = ((last_close - first_close) / first_close) * 100
return f"{move:+.2f}%"
def _build_dynamic_chart(history: pd.DataFrame, timeframe: str) -> tuple[go.Figure, str, str]:
window = _slice_history_window(history, timeframe)
if window.empty:
return go.Figure(), "N/A", COLORS["text_muted"]
first_close = float(window["close"].iloc[0])
last_close = float(window["close"].iloc[-1])
line_color = COLORS["green"] if last_close >= first_close else COLORS["red"]
move_text = _format_move_pct(window)
template = dict(get_plotly_template())
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=history["date"],
y=history["close"],
mode="lines",
line=dict(color="rgba(148,163,184,0.22)", width=1.1),
hovertemplate="%{x|%b %d, %Y}
$%{y:.2f}",
name="History",
)
)
fig.add_trace(
go.Scatter(
x=window["date"],
y=window["close"],
mode="lines",
line=dict(color=line_color, width=2.8),
fill="tozeroy",
fillcolor=(
"rgba(34,197,94,0.18)" if line_color == COLORS["green"] else "rgba(239,68,68,0.18)"
),
hovertemplate=f"{timeframe}
%{{x|%b %d, %Y}}
$%{{y:.2f}}",
name=f"{timeframe} Focus",
)
)
# Override template keys before expansion to avoid duplicate keyword args.
template["height"] = 210
template["showlegend"] = False
template["margin"] = dict(l=0, r=0, t=10, b=0)
fig.update_layout(**template)
# Tighten Y-axis to selected timeframe range for better signal visibility.
y_min = float(window["close"].min())
y_max = float(window["close"].max())
if y_min == y_max:
pad = max(0.5, y_min * 0.01)
else:
pad = max((y_max - y_min) * 0.08, y_max * 0.01)
fig.update_xaxes(
showticklabels=False,
showgrid=False,
range=[window["date"].min(), history["date"].max()],
rangeslider=dict(visible=False),
)
fig.update_yaxes(
showgrid=True,
gridcolor="rgba(42,53,72,0.28)",
tickprefix="$",
nticks=5,
range=[y_min - pad, y_max + pad],
)
return fig, move_text, line_color
def _render_single_dynamic_chart(ticker: str, timeframe: str) -> None:
base_history = _load_price_history(ticker, "1y")
if base_history.empty:
st.caption("No price history available for this ticker.")
return
window = _slice_history_window(base_history, timeframe)
if window.empty:
st.caption(f"Not enough data to render {timeframe} window.")
return
fig, move_text, move_color = _build_dynamic_chart(base_history, timeframe)
st.markdown(
f"""
Dynamic Price View • {timeframe}
{move_text}
""",
unsafe_allow_html=True,
)
st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
def render():
today = datetime.now().strftime("%Y-%m-%d")
recommendations, meta = load_recommendations(today, return_meta=True)
display_date = meta.get("date", today) if meta else today
st.markdown(
page_header("Signals", f"Recommendations for {display_date}"),
unsafe_allow_html=True,
)
if not recommendations:
st.warning(f"No recommendations for {today}.")
return
if meta.get("is_fallback") and meta.get("date"):
st.info(f"Showing latest signals from **{meta['date']}** (none for today).")
# ---- Controls row ----
ctrl_cols = st.columns([1, 1, 1, 1])
with ctrl_cols[0]:
pipelines = sorted(
set(
(r.get("pipeline") or r.get("strategy_match") or "unknown") for r in recommendations
)
)
pipeline_filter = st.multiselect("Strategy", pipelines, default=pipelines)
with ctrl_cols[1]:
min_confidence = st.slider("Min Confidence", 1, 10, 1)
with ctrl_cols[2]:
min_score = st.slider("Min Score", 0, 100, 0)
with ctrl_cols[3]:
show_charts = st.checkbox("Price Charts", value=False)
# Apply filters
filtered = [
r
for r in recommendations
if (r.get("pipeline") or r.get("strategy_match") or "unknown") in pipeline_filter
and r.get("confidence", 0) >= min_confidence
and r.get("final_score", 0) >= min_score
]
# ---- Summary bar ----
st.markdown(
f"""
Showing
{len(filtered)} of {len(recommendations)} signals
{display_date}
""",
unsafe_allow_html=True,
)
# ---- Signal cards in 2-column grid ----
for i in range(0, len(filtered), 2):
cols = st.columns(2)
for j, col in enumerate(cols):
idx = i + j
if idx >= len(filtered):
break
rec = filtered[idx]
ticker = rec.get("ticker", "UNKNOWN")
rank = rec.get("rank", idx + 1)
score = rec.get("final_score", 0)
confidence = rec.get("confidence", 0)
strategy = (rec.get("pipeline") or rec.get("strategy_match") or "unknown").title()
entry_price = rec.get("entry_price", 0)
reason = rec.get("reason", "No thesis provided.")
with col:
st.markdown(
signal_card(rank, ticker, score, confidence, strategy, entry_price, reason),
unsafe_allow_html=True,
)
if show_charts:
st.markdown(
f"""
Chart Timeframe
""",
unsafe_allow_html=True,
)
chart_timeframe = st.radio(
f"Timeframe for {ticker}",
list(TIMEFRAME_LOOKBACK_DAYS.keys()),
index=2,
horizontal=True,
label_visibility="collapsed",
key=f"chart_tf_{ticker}_{idx}",
)
_render_single_dynamic_chart(ticker, chart_timeframe)
# Action buttons
btn_cols = st.columns(2)
with btn_cols[0]:
if st.button("Enter Position", key=f"enter_{ticker}_{idx}"):
st.info(f"Position entry for {ticker} (TODO)")
with btn_cols[1]:
if st.button("Watchlist", key=f"watch_{ticker}_{idx}"):
st.success(f"Added {ticker} to watchlist")