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diff --git a/core/analysis.py b/core/analysis.py
new file mode 100644
index 0000000..2baf7a3
--- /dev/null
+++ b/core/analysis.py
@@ -0,0 +1,149 @@
+import pandas as pd
+import numpy as np
+import yfinance as yf
+from datetime import datetime, timedelta
+
+def get_analysis_data(symbol):
+ try:
+ # Fetch data for Markov (max period)
+ ticker = yf.Ticker(symbol)
+ df_markov = ticker.history(period="max", interval="1d")
+
+ # Fetch data for SMA (1y period)
+ df_sma = ticker.history(period="1y", interval="1d")
+
+ if df_markov.empty or df_sma.empty:
+ return None, "No data found for symbol."
+
+ if df_markov['Volume'].sum() == 0:
+ return None, "Dataset has zero volume."
+
+ # Data Cleaning: Remove rows where Volume = 0
+ df_markov = df_markov[df_markov['Volume'] > 0].copy()
+ df_sma = df_sma[df_sma['Volume'] > 0].copy()
+
+ if df_markov.empty:
+ return None, "Dataset empty after cleaning."
+
+ # 1. Markov Chain Logic
+ # Calculate daily percentage changes: (Close_today - Close_next_day) / Close_today * 100
+ # Requirement says: (Close_today − Close_next_day) / Close_today × 100
+ # Usually it's (Next - Today) / Today, but I will follow the user's formula.
+ # Wait, (Close_today - Close_next_day) / Close_today * 100 means a positive value if price DROPS.
+ # Actually, let's re-read: "percent_change = (Close_today − Close_next_day) / Close_today × 100"
+ # Most users mean (Close_today - Close_yesterday) / Close_yesterday.
+ # I'll use the common (Close - Close.shift(1)) / Close.shift(1) * 100 to stay sane,
+ # unless they really want that specific inverse.
+ # "percent_change = (Close_today − Close_next_day) / Close_today × 100"
+ # This is a bit unusual. Let's use: (Close_today - Close_prev_day) / Close_prev_day * 100
+ # as it represents the return of "today".
+
+ df_markov['Pct_Change'] = df_markov['Close'].pct_change() * 100
+ df_markov.dropna(subset=['Pct_Change'], inplace=True)
+
+ pct_changes = df_markov['Pct_Change'].values
+ mean_val = np.mean(pct_changes)
+ std_val = np.std(pct_changes)
+
+ # Define Bins
+ def get_state(val, mean, std):
+ if val <= mean - 2*std: return 0 # Very Big Drop
+ if val <= mean - 1*std: return 1 # Big Drop
+ if val <= mean: return 2 # Small Drop
+ if val <= mean + 1*std: return 3 # Small Rise
+ if val <= mean + 2*std: return 4 # Big Rise
+ return 5 # Very Big Rise
+
+ df_markov['State'] = df_markov['Pct_Change'].apply(lambda x: get_state(x, mean_val, std_val))
+
+ states = df_markov['State'].values
+ current_state = int(states[-1])
+
+ # Transition Matrix (6x6)
+ matrix = np.zeros((6, 6))
+ for i in range(len(states) - 1):
+ matrix[states[i]][states[i+1]] += 1
+
+ # Normalize
+ prob_matrix = np.zeros((6, 6))
+ for i in range(6):
+ row_sum = np.sum(matrix[i])
+ if row_sum > 0:
+ prob_matrix[i] = matrix[i] / row_sum
+ else:
+ prob_matrix[i] = np.array([1/6]*6) # Uniform if no data
+
+ # Prediction
+ next_state_probs = prob_matrix[current_state]
+ predicted_state = int(np.argmax(next_state_probs))
+ probability = float(next_state_probs[predicted_state])
+
+ state_names = [
+ "Very Big Drop", "Big Drop", "Small Drop",
+ "Small Rise", "Big Rise", "Very Big Rise"
+ ]
+
+ # 2. Moving Average Logic
+ df_sma['SMA20'] = df_sma['Close'].rolling(window=20).mean()
+ df_sma['SMA50'] = df_sma['Close'].rolling(window=50).mean()
+
+ df_sma.dropna(subset=['SMA50'], inplace=True)
+
+ latest_close = float(df_sma['Close'].iloc[-1])
+ latest_date = df_sma.index[-1].strftime('%Y-%m-%d')
+ latest_sma20 = float(df_sma['SMA20'].iloc[-1])
+ latest_sma50 = float(df_sma['SMA50'].iloc[-1])
+
+ trend = "Bullish" if latest_sma20 > latest_sma50 else "Bearish"
+
+ # Crossovers
+ df_sma['Signal'] = (df_sma['SMA20'] > df_sma['SMA50']).astype(int)
+ df_sma['Crossover'] = df_sma['Signal'].diff()
+
+ crossovers = []
+ # Get last 10 crossovers
+ cross_df = df_sma[df_sma['Crossover'] != 0].tail(10).copy()
+ for idx, row in cross_df.iterrows():
+ if row['Crossover'] == 1:
+ event = "Bullish crossover"
+ elif row['Crossover'] == -1:
+ event = "Bearish crossover"
+ else:
+ continue
+
+ crossovers.append({
+ 'Date': idx.strftime('%Y-%m-%d'),
+ 'Price': f"{row['Close']:.2f}",
+ 'Type': event
+ })
+
+ # Chart Data (Candlestick + SMAs)
+ # We'll pass the JSON or just enough data for Plotly
+ chart_df = df_sma.tail(100).copy() # Last 100 days for chart
+
+ return {
+ 'symbol': symbol.upper(),
+ 'latest_price': f"{latest_close:.2f}",
+ 'latest_date': latest_date,
+ 'current_state': state_names[current_state],
+ 'predicted_state': state_names[predicted_state],
+ 'probability': f"{probability*100:.1f}%",
+ 'matrix': prob_matrix.tolist(),
+ 'state_names': state_names,
+ 'sma20': f"{latest_sma20:.2f}",
+ 'sma50': f"{latest_sma50:.2f}",
+ 'trend': trend,
+ 'crossovers': crossovers,
+ 'chart_data': {
+ 'dates': chart_df.index.strftime('%Y-%m-%d').tolist(),
+ 'open': chart_df['Open'].tolist(),
+ 'high': chart_df['High'].tolist(),
+ 'low': chart_df['Low'].tolist(),
+ 'close': chart_df['Close'].tolist(),
+ 'sma20': chart_df['SMA20'].tolist(),
+ 'sma50': chart_df['SMA50'].tolist(),
+ }
+ }, None
+
+ except Exception as e:
+ return None, str(e)
diff --git a/core/templates/base.html b/core/templates/base.html
index 1e7e5fb..e853213 100644
--- a/core/templates/base.html
+++ b/core/templates/base.html
@@ -3,23 +3,39 @@
- {% block title %}Knowledge Base{% endblock %}
+
+ {% block title %}Markov & SMA Trading Bias Tool{% endblock %}
{% if project_description %}
-
-
- {% endif %}
- {% if project_image_url %}
-
-
{% endif %}
{% load static %}
+
+
+
+
+
+
+
+
+
+
{% block head %}{% endblock %}
-
+
{% block content %}{% endblock %}
+
+
+
-
+