Rename App.py to app.py

App.py

@@ -1,51 +0,0 @@
-import gradio as gr
-from src.data_fetcher import fetch_crypto_data, fetch_stock_data, fetch_sentiment_data
-from src.model import train_model, predict_growth
-from src.visualizer import plot_price_trends, plot_sentiment_trends
-
-def analyze_asset(asset_type, symbol):
-    """
-    Analyze a stock or cryptocurrency symbol.
-    Fetches market data, performs sentiment analysis,
-    trains AI model, and provides predictions with visualizations.
-    """
-    try:
-        if asset_type == "Crypto":
-            market_data = fetch_crypto_data(symbol)
-        elif asset_type == "Stock":
-            market_data = fetch_stock_data(symbol)
-        else:
-            return "Invalid asset type."
-
-        sentiment_score = fetch_sentiment_data(symbol)
-        train_model(market_data)
-        latest_data = market_data.iloc[-1][["close", "volume"]].values.tolist()
-        prediction = predict_growth(latest_data)
-
-        price_plot = plot_price_trends(market_data)
-        sentiment_plot = plot_sentiment_trends(symbol)
-
-        action = "BUY" if prediction == 1 else "SELL"
-        result = {
-            "Symbol": symbol,
-            "Sentiment Score": f"{sentiment_score:.2f}",
-            "Prediction": action,
-            "Visualizations": [price_plot, sentiment_plot],
-        }
-        return result
-
-    except Exception as e:
-        return {"Error": str(e)}
-
-# Gradio Interface
-asset_type_input = gr.Radio(["Crypto", "Stock"], label="Asset Type")
-symbol_input = gr.Textbox(label="Enter Symbol (e.g., BTCUSDT or AAPL)")
-output_result = gr.JSON(label="Analysis Results")
-
-gr.Interface(
-    fn=analyze_asset,
-    inputs=[asset_type_input, symbol_input],
-    outputs=output_result,
-    title="Explosive Growth Bot",
-    description="Predicts explosive growth in stocks and cryptocurrencies using AI.",
-).launch()

app.py

@@ -0,0 +1,119 @@
+import gradio as gr
+import pandas as pd
+import requests
+from prophet import Prophet
+import plotly.graph_objs as go
+import math
+import numpy as np
+
+# Constants for API endpoints
+OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
+OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"
+
+TIMEFRAME_MAPPING = {
+    "1m": "1m",
+    "5m": "5m",
+    "15m": "15m",
+    "30m": "30m",
+    "1h": "1H",
+    "2h": "2H",
+    "4h": "4H",
+    "6h": "6H",
+    "12h": "12H",
+    "1d": "1D",
+    "1w": "1W",
+}
+
+# Function to calculate technical indicators
+def calculate_technical_indicators(df):
+    # RSI Calculation
+    delta = df['close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    df['RSI'] = 100 - (100 / (1 + rs))
+    
+    # MACD Calculation
+    exp1 = df['close'].ewm(span=12, adjust=False).mean()
+    exp2 = df['close'].ewm(span=26, adjust=False).mean()
+    df['MACD'] = exp1 - exp2
+    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    
+    # Bollinger Bands Calculation
+    df['MA20'] = df['close'].rolling(window=20).mean()
+    df['BB_upper'] = df['MA20'] + 2 * df['close'].rolling(window=20).std()
+    df['BB_lower'] = df['MA20'] - 2 * df['close'].rolling(window=20).std()
+    
+    return df
+
+# Function to create technical analysis charts
+def create_technical_charts(df):
+    # Price and Bollinger Bands Chart
+    fig1 = go.Figure()
+    fig1.add_trace(go.Candlestick(
+        x=df['timestamp'],
+        open=df['open'],
+        high=df['high'],
+        low=df['low'],
+        close=df['close'],
+        name='Price'
+    ))
+    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_upper'], name='Upper BB', line=dict(color='gray', dash='dash')))
+    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_lower'], name='Lower BB', line=dict(color='gray', dash='dash')))
+    fig1.update_layout(title='Price and Bollinger Bands', xaxis_title='Date', yaxis_title='Price')
+
+    # RSI Chart
+    fig2 = go.Figure()
+    fig2.add_trace(go.Scatter(x=df['timestamp'], y=df['RSI'], name='RSI'))
+    fig2.add_hline(y=70, line_dash="dash", line_color="red")
+    fig2.add_hline(y=30, line_dash="dash", line_color="green")
+    fig2.update_layout(title='RSI Indicator', xaxis_title='Date', yaxis_title='RSI')
+
+    # MACD Chart
+    fig3 = go.Figure()
+    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['MACD'], name='MACD'))
+    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['Signal_Line'], name='Signal Line'))
+    fig3.update_layout(title='MACD', xaxis_title='Date', yaxis_title='Value')
+
+    return fig1, fig2, fig3
+
+# Fetch available symbols from OKX API
+def fetch_okx_symbols():
+    try:
+        resp = requests.get(OKX_TICKERS_ENDPOINT)
+        data = resp.json().get("data", [])
+        symbols = [item["instId"] for item in data if item.get("instType") == "SPOT"]
+        return ["BTC-USDT"] + symbols if symbols else ["BTC-USDT"]
+    except Exception as e:
+        return ["BTC-USDT"]
+
+# Fetch historical candle data from OKX API
+def fetch_okx_candles(symbol, timeframe="1H", total=2000):
+    calls_needed = math.ceil(total / 300)
+    all_data = []
+    
+    for _ in range(calls_needed):
+        params = {"instId": symbol, "bar": timeframe, "limit": 300}
+        resp = requests.get(OKX_CANDLE_ENDPOINT, params=params)
+        data = resp.json().get("data", [])
+        
+        if not data:
+            break
+        
+        columns = ["ts", "o", "h", "l", "c"]
+        df_chunk = pd.DataFrame(data, columns=columns)
+        df_chunk.rename(columns={"ts": "timestamp", "o": "open", 
+                                  "h": "high", "l": "low", 
+                                  "c": "close"}, inplace=True)
+        all_data.append(df_chunk)
+
+        if len(data) < 300:
+            break
+    
+    if not all_data:
+        return pd.DataFrame()
+
+    df_all = pd.concat(all_data)
+    
+   # Convert timestamps to datetime and calculate indicators
+   ...