New tabs and functions v1.3

core/crypto_dashboard.py

@@ -0,0 +1,106 @@
+"""
+🇬🇧 Module: crypto_dashboard.py
+Purpose: Build Power BI–style dashboard for crypto market using CoinGecko API + portfolio metrics.
+🇷🇺 Модуль: crypto_dashboard.py
+Назначение: Создание Power BI-подобного дашборда на основе CoinGecko API и метрик портфеля.
+"""
+
+import requests
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+from datetime import datetime
+from services.output_api import fetch_metrics_async
+from services.llm_client import llm_service
+import asyncio
+
+COINGECKO_API = "https://api.coingecko.com/api/v3"
+
+
+async def _get_portfolio_metrics(portfolio_id):
+    metrics = await fetch_metrics_async(portfolio_id)
+    return metrics or {}
+
+
+def get_market_data(coin_id: str = "bitcoin", days: int = 30):
+    """Fetch market data (price, volume, market cap) from CoinGecko."""
+    url = f"{COINGECKO_API}/coins/{coin_id}/market_chart?vs_currency=usd&days={days}"
+    r = requests.get(url)
+    data = r.json()
+    df = pd.DataFrame(data["prices"], columns=["timestamp", "price"])
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+    df["returns"] = df["price"].pct_change()
+    df["volatility"] = df["returns"].rolling(7).std() * 100
+    return df
+
+
+def build_dashboard(coin_id: str, portfolio_id: str, days: int = 30):
+    """Main sync wrapper: returns 3 Plotly figures + LLM commentary."""
+    df = get_market_data(coin_id, days)
+    metrics = asyncio.run(_get_portfolio_metrics(portfolio_id))
+
+    if df.empty:
+        return None, None, None, "❌ No market data."
+
+    # === KPI Cards ===
+    growth = (df["price"].iloc[-1] / df["price"].iloc[0] - 1) * 100
+    vol = df["volatility"].mean()
+    sharpe = metrics.get("sharpe", 0)
+
+    kpi = go.Figure()
+    kpi.add_trace(go.Indicator(
+        mode="number+delta",
+        value=round(growth, 2),
+        delta={"reference": 0, "position": "right"},
+        title={"text": f"{coin_id.capitalize()} Growth %"},
+        domain={'row': 0, 'column': 0}
+    ))
+    kpi.add_trace(go.Indicator(
+        mode="number",
+        value=round(vol, 2),
+        title={"text": "Volatility %"},
+        domain={'row': 0, 'column': 1}
+    ))
+    kpi.add_trace(go.Indicator(
+        mode="number",
+        value=round(sharpe, 2),
+        title={"text": "Sharpe (Portfolio)"},
+        domain={'row': 0, 'column': 2}
+    ))
+    kpi.update_layout(grid={'rows': 1, 'columns': 3}, template="plotly_dark", height=200)
+
+    # === Price timeline ===
+    price_fig = px.line(df, x="timestamp", y="price", title=f"{coin_id.capitalize()} Price (USD)")
+    price_fig.update_layout(template="plotly_dark", height=400)
+
+    # === Volatility chart ===
+    vol_fig = px.area(df, x="timestamp", y="volatility", title="7-Day Rolling Volatility")
+    vol_fig.update_traces(line_color="#4f46e5")
+    vol_fig.update_layout(template="plotly_dark", height=300)
+
+    # === Generate AI summary ===
+    commentary = _generate_ai_comment(df, coin_id, metrics)
+    return kpi, price_fig, vol_fig, commentary
+
+
+def _generate_ai_comment(df, coin_id, metrics):
+    """Generate an AI summary (LLM commentary)."""
+    change = (df["price"].iloc[-1] / df["price"].iloc[0] - 1) * 100
+    avg_vol = df["volatility"].mean()
+    prompt = f"""
+Act as a financial analyst.
+Summarize {coin_id.capitalize()} market performance over the last period.
+
+- Growth: {change:.2f}%
+- Average Volatility: {avg_vol:.2f}%
+- Portfolio Sharpe: {metrics.get('sharpe', 0):.2f}
+
+Provide 3–5 concise sentences: performance, stability, and potential outlook.
+"""
+    commentary = ""
+    for delta in llm_service.stream_chat(
+        messages=[{"role": "user", "content": prompt}],
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct"
+    ):
+        commentary += delta
+    return commentary

core/visual_metrics.py

@@ -1,81 +0,0 @@
-"""
-🇬🇧 Module: visual_metrics.py!
-Purpose: DAX-style KPI calculations and interactive charts for dashboards.
-🇷🇺 Модуль: visual_metrics.py
-Назначение: DAX-подобные вычисления KPI и построение интерактивных графиков.
-"""
-
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from services.output_api import fetch_metrics_async, fetch_absolute_pnl_async
-import asyncio
-
-
-async def _load_full_data(portfolio_id: str):
-    metrics = await fetch_metrics_async(portfolio_id)
-    pnl = await fetch_absolute_pnl_async(portfolio_id)
-    df = pd.DataFrame(pnl)
-    if "value" in df:
-        df["value"] = pd.to_numeric(df["value"], errors="coerce")
-    return metrics, df
-
-
-def dax_return_growth(df: pd.DataFrame) -> float:
-    """📈 Аналог DAX: CALCULATE( SUM(Profit) / EARLIER(TotalProfit) )"""
-    if df.empty or "value" not in df:
-        return 0
-    return (df["value"].iloc[-1] - df["value"].iloc[0]) / abs(df["value"].iloc[0]) * 100
-
-
-def dax_volatility(df: pd.DataFrame) -> float:
-    """📊 Аналог DAX: STDEVX.P(VALUES(Returns))"""
-    if df.empty or "value" not in df:
-        return 0
-    return df["value"].pct_change().std() * 100
-
-
-async def build_dashboard_figures(portfolio_id: str):
-    """Возвращает набор графиков и KPI, имитирующих Power BI Dashboard."""
-    metrics, df = await _load_full_data(portfolio_id)
-
-    if df.empty:
-        return None, None, None
-
-    kpi_return = dax_return_growth(df)
-    kpi_vol = dax_volatility(df)
-    sharpe = metrics.get("sharpe", 0)
-
-    # === KPI Cards ===
-    kpi_cards = go.Figure()
-    kpi_cards.add_trace(go.Indicator(
-        mode="number+delta",
-        value=round(kpi_return, 2),
-        delta={"reference": 0, "position": "right"},
-        title={"text": "Portfolio Growth %"},
-        domain={'row': 0, 'column': 0}
-    ))
-    kpi_cards.add_trace(go.Indicator(
-        mode="number",
-        value=round(kpi_vol, 2),
-        title={"text": "Volatility %"},
-        domain={'row': 0, 'column': 1}
-    ))
-    kpi_cards.add_trace(go.Indicator(
-        mode="number",
-        value=round(sharpe, 2),
-        title={"text": "Sharpe Ratio"},
-        domain={'row': 0, 'column': 2}
-    ))
-    kpi_cards.update_layout(grid={'rows': 1, 'columns': 3}, template="plotly_dark")
-
-    # === PnL Timeline Chart ===
-    pnl_fig = px.line(df, y="value", title="Absolute PnL Over Time")
-    pnl_fig.update_layout(template="plotly_dark", height=400)
-
-    # === Histogram Volatility ===
-    vol_fig = px.histogram(df["value"].pct_change().dropna(),
-                           nbins=30, title="Daily Returns Distribution")
-    vol_fig.update_layout(template="plotly_dark", height=300)
-
-    return kpi_cards, pnl_fig, vol_fig