New tabs and functions v2.0

app.py

@@ -97,31 +97,27 @@ with gr.Blocks(theme=dark_theme, css="""
             chart_out = gr.Plot(label="Alpha vs BTC")
             chart_btn.click(fn=build_alpha_chart, inputs=chart_in, outputs=chart_out)
 
-        # --- Crypto Intelligence Dashboard (refactored) ---
+        # --- Crypto Intelligence Dashboard (final Power BI style) ---
         with gr.TabItem("Crypto Intelligence Dashboard"):
-            gr.Markdown("### 📊 Unified Market Intelligence (Binance + TradingView)")
+            gr.Markdown("### 💹 Multi-Source Market Dashboard")
 
             with gr.Row():
-                assets_select = gr.CheckboxGroup(
-                    label="Select assets",
-                    choices=["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT", "DOGEUSDT"],
-                    value=["BTCUSDT", "ETHUSDT"]
-                )
-                start_picker = gr.DatePicker(label="Start Date", value=(datetime.now() - timedelta(days=365*2)))
-                end_picker = gr.DatePicker(label="End Date", value=datetime.now())
-                load_btn = gr.Button("Load Market Data", variant="primary")
-
-            price_plot = gr.Plot(label="Recent Market (Binance)")
-            hist_plot = gr.Plot(label="Historical Chart (TradingView)")
-            ai_summary = gr.Textbox(label="AI Market Summary", lines=8, elem_id="llm_comment_box")
+                data_source = gr.Radio(
+                    ["Binance", "Coinlore", "Yahoo"], label="Select Data Source", value="Binance")
+                asset_select = gr.CheckboxGroup(
+                    label="Assets", choices=["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT", "DOGEUSDT"],
+                    value=["BTCUSDT", "ETHUSDT"])
+                load_btn = gr.Button("Build Dashboard", variant="primary")
+
+            chart = gr.Plot(label="Market Visualization")
+            ai_box = gr.Textbox(label="AI Summary", lines=8, elem_id="llm_comment_box")
 
             from core.crypto_dashboard import build_crypto_dashboard
-            load_btn.click(
-                fn=build_crypto_dashboard,
-                inputs=[assets_select, start_picker, end_picker],
-                outputs=[price_plot, hist_plot, ai_summary],
-                show_progress="minimal",
-            )
+
+            load_btn.click(fn=build_crypto_dashboard,
+                           inputs=[data_source, asset_select],
+                           outputs=[chart, ai_box],
+                           show_progress="minimal")
 
     gr.Markdown("---")
     gr.Markdown(

core/crypto_dashboard.py

@@ -1,111 +1,42 @@
 """
-🇬🇧 Module: crypto_dashboard.py
-Purpose: Unified dashboard for live & historical crypto analytics (Binance + Yahoo Finance)
-🇷🇺 Модуль: crypto_dashboard.py
-Назначение: Единый дашборд для анализа крипторынка на Binance и Yahoo.
+Power-BI-style dashboard combining Binance, Coinlore, and Yahoo Finance
 """
-
-import requests
-import pandas as pd
-import yfinance as yf
-import plotly.express as px
-import plotly.graph_objects as go
+import plotly.express as px, plotly.graph_objects as go
 from services.llm_client import llm_service
-
-BINANCE_API = "https://api.binance.com/api/v3"
-
-
-# === Binance data (live candles) ===
-def get_binance_klines(symbol: str = "BTCUSDT", interval="1d", limit=180):
-    url = f"{BINANCE_API}/klines?symbol={symbol}&interval={interval}&limit={limit}"
-    data = requests.get(url).json()
-    df = pd.DataFrame(data, columns=[
-        "timestamp", "open", "high", "low", "close", "volume",
-        "close_time", "qav", "trades", "tbbav", "tbqav", "ignore"
-    ])
-    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
-    df[["open", "high", "low", "close", "volume"]] = df[["open", "high", "low", "close", "volume"]].astype(float)
-    return df
-
-
-# === Yahoo Finance historical data ===
-def get_yf_history(symbols: list[str]):
-    frames = []
-    for s in symbols:
-        try:
-            ticker = s.replace("USDT", "-USD")
-            df = yf.download(ticker, period="5y", interval="1d", progress=False)
-            df = df.reset_index()[["Date", "Close"]]
-            df.columns = ["datetime", "close"]
-            df["symbol"] = s
-            frames.append(df)
-        except Exception:
-            continue
-    if not frames:
-        return pd.DataFrame()
-    return pd.concat(frames, ignore_index=True)
-
-
-# === Build Dashboard ===
-def build_crypto_dashboard(selected_assets: list[str], start_date: str, end_date: str):
-    if not selected_assets:
-        selected_assets = ["BTCUSDT", "ETHUSDT", "BNBUSDT"]
-
-    # --- Binance recent ---
-    recent_frames = []
-    for sym in selected_assets:
-        df = get_binance_klines(sym, "1d", 180)
-        recent_frames.append(df.assign(symbol=sym))
-    df_recent = pd.concat(recent_frames)
-
-    fig_recent = px.line(
-        df_recent, x="timestamp", y="close", color="symbol",
-        title="Market Prices (Binance, recent 6 months)",
-        template="plotly_dark"
-    )
-    fig_recent.update_layout(height=420, legend_title_text="Asset")
-
-    # --- Yahoo historical normalized ---
-    df_hist = get_yf_history(selected_assets)
-    if not df_hist.empty:
-        df_hist["returns"] = df_hist.groupby("symbol")["close"].apply(lambda x: x / x.iloc[0] - 1)
-        fig_hist = px.line(df_hist, x="datetime", y="returns", color="symbol",
-                           title="Historical Normalized Performance (Yahoo Finance)",
-                           template="plotly_dark")
-        fig_hist.update_layout(height=420)
+from core.data_binance import get_binance_data
+from core.data_coinlore import get_coinlore_data
+from core.data_yfinance import get_yf_history
+
+def build_crypto_dashboard(source: str, symbols: list[str]):
+    if source == "Binance":
+        df = get_binance_data(symbols)
+        fig = px.line(df, x="timestamp", y="close", color="symbol",
+                      title="Binance Price History (1y)", template="plotly_dark")
+    elif source == "Yahoo":
+        df = get_yf_history([s.replace("USDT","-USD") for s in symbols])
+        df["returns"] = df.groupby("symbol")["close"].apply(lambda x: x/x.iloc[0]-1)
+        fig = px.line(df, x="timestamp", y="returns", color="symbol",
+                      title="Yahoo Finance – Normalized Returns (2y)", template="plotly_dark")
+    elif source == "Coinlore":
+        df = get_coinlore_data().head(25)
+        fig = px.treemap(df,
+                         path=["symbol"], values="market_cap_usd",
+                         color="percent_change_24h",
+                         color_continuous_scale="RdYlGn",
+                         title="Coinlore Market Treemap (Top 25)",
+                         template="plotly_dark")
     else:
-        fig_hist = go.Figure()
-        fig_hist.add_annotation(
-            text="No Yahoo Finance data available",
-            xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False
-        )
-
-    # --- AI Market Summary ---
-    summary = _generate_ai_summary(df_recent, df_hist, selected_assets)
-
-    return fig_recent, fig_hist, summary
-
+        fig = go.Figure()
+        fig.add_annotation(text="Unknown data source", x=0.5, y=0.5, showarrow=False)
 
-def _generate_ai_summary(df_recent, df_hist, assets):
-    if df_recent.empty:
-        return "❌ No data available to summarize."
+    insight = _generate_ai_summary(source, symbols)
+    return fig, insight
 
-    growth = []
-    for a in assets:
-        sub = df_recent[df_recent["symbol"] == a]
-        if len(sub) > 5:
-            g = (sub["close"].iloc[-1] / sub["close"].iloc[0] - 1) * 100
-            growth.append(f"{a}: {g:.2f}%")
-    growth_text = ", ".join(growth)
 
+def _generate_ai_summary(source, symbols):
     prompt = f"""
-Analyze the crypto market based on these assets: {', '.join(assets)}.
-Performance (last 6 months): {growth_text}.
-Give a concise professional summary:
-- Market direction (bullish/bearish)
-- Volatility trends
-- Leaders vs laggards
-- Short-term outlook
+Summarize crypto market from {source} for assets: {', '.join(symbols)}.
+Describe trend, volatility, and investor sentiment.
 """
     summary = ""
     for delta in llm_service.stream_chat(

core/data_binance.py

@@ -0,0 +1,22 @@
+"""
+Binance API Handler – public OHLC data
+"""
+import requests, pandas as pd
+
+BINANCE = "https://api.binance.com/api/v3"
+
+def get_binance_data(symbols=None, limit=500):
+    if symbols is None:
+        symbols = ["BTCUSDT", "ETHUSDT", "BNBUSDT"]
+    frames = []
+    for s in symbols:
+        url = f"{BINANCE}/klines?symbol={s}&interval=1d&limit={limit}"
+        r = requests.get(url).json()
+        df = pd.DataFrame(r, columns=[
+            "t","o","h","l","c","v","ct","qv","tr","tb","tbv","ig"])
+        df = df[["t","c"]].rename(columns={"t":"timestamp","c":"close"})
+        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+        df["close"] = df["close"].astype(float)
+        df["symbol"] = s
+        frames.append(df)
+    return pd.concat(frames)

core/data_coinlore.py

@@ -0,0 +1,13 @@
+"""
+Coinlore API Handler – market overview
+"""
+import requests, pandas as pd
+
+def get_coinlore_data():
+    url = "https://api.coinlore.net/api/tickers/"
+    data = requests.get(url).json()["data"]
+    df = pd.DataFrame(data)
+    df["price_usd"] = df["price_usd"].astype(float)
+    df["market_cap_usd"] = df["market_cap_usd"].astype(float)
+    df["percent_change_24h"] = df["percent_change_24h"].astype(float)
+    return df

core/data_yfinance.py

@@ -0,0 +1,16 @@
+"""
+Yahoo Finance API Handler – historical 5-10 years
+"""
+import yfinance as yf, pandas as pd
+
+def get_yf_history(symbols=None, period="2y"):
+    if symbols is None:
+        symbols = ["BTC-USD","ETH-USD","BNB-USD"]
+    frames = []
+    for s in symbols:
+        df = yf.download(s, period=period, interval="1d", progress=False)
+        df = df.reset_index()[["Date","Close"]]
+        df.columns = ["timestamp","close"]
+        df["symbol"] = s.replace("-USD","USDT")
+        frames.append(df)
+    return pd.concat(frames)