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literal 1364 zcmZ`(&1)M+6ra_{dS%J>I!c! z|D2kdL_iDu-_09gg#HmqK%t><`ZWm8kbw-zLiw{@%1fY!tdJefM7AQl zBhn19Sa6Ea_&F0f52+#t(zs7E+?3c%aE4Gt31lN`$VZUXaWDY%0l$fN{S4($*^jWf zAQFOZ8L?*M2p%0Y9=dd7Ae9|G&s?)vuje~ z*w|s3fmzYCyi6leblsZSXw)-0vAJ@~b$=8ZkKMM4%pxUIZ^X86P1|903-h@L+xPEo zRqQc33p$+=>?7eip%aJUJ?4psnxMCzWy{3I+3KUJ!>U=lhxIBWG+QI|iRTt| zob~hDHF3?$xOE;8-gF7B3n+Sj0BfPw>ijEJJ5jZcy3$owUa6}m>S{+#chz)jv!|tA z{CuKaZ)?{N(g*36i>>ealjojZ{Vnq})5;BG^zo;?`IUZhwUbPDlj(kPrJMYslf2eV zUh6F^_AXrPFJC&Ww^QpcL!ISZcRANzT0Z=#edVjazv(R9>@M9L%*JNq0Rq;#^X5EC zoIAMEUcCCE)QPWk<7@56+9@On0Hh1B(RpB8KAS2P06puedS=OZWkMOW!1G(l6GL;A z0R;Uy__hCq=tXh$6JT$Z8#Xbj7G9^9K^1=i?=i4JSdye3inaW&hh|#-_ZofP9$!6l Xxjnvm=u&%p4P;gNa)91vejfh;ew 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 %} + + + - + \ No newline at end of file diff --git a/core/templates/core/article_detail.html b/core/templates/core/article_detail.html index 8820990..56119e2 100644 --- a/core/templates/core/article_detail.html +++ b/core/templates/core/article_detail.html @@ -5,7 +5,7 @@ {% block content %}

{{ article.title }}

-

Published on {{ article.created_at|date:"F d, Y" }}

+

Published on {{ article.created_at|date:"F d, Y" }}

{{ article.content|safe }} diff --git a/core/templates/core/index.html b/core/templates/core/index.html index faec813..40b8807 100644 --- a/core/templates/core/index.html +++ b/core/templates/core/index.html @@ -1,145 +1,258 @@ -{% extends "base.html" %} - -{% block title %}{{ project_name }}{% endblock %} - -{% block head %} - - - - -{% endblock %} +{% extends 'base.html' %} +{% load static %} {% block content %} -
-
-

Analyzing your requirements and generating your app…

-
- Loading… +
+ + +
+
+

+ Markov Chain & SMA Tool +

+

Analyse daily bias with statistical Markov probabilities and SMA crossovers.

+
+ {% csrf_token %} + + +
+
+ + Analysing market data... +
+ {% if history %} +
+ Recent: + {% for h in history %} +
+ {% csrf_token %} + + +
+ {% endfor %} +
+ {% endif %} +
-

AppWizzy AI is collecting your requirements and applying the first changes.

-

This page will refresh automatically as the plan is implemented.

-

- Runtime: Django {{ django_version }} · Python {{ python_version }} - — UTC {{ current_time|date:"Y-m-d H:i:s" }} -

-
-
-
- Page updated: {{ current_time|date:"Y-m-d H:i:s" }} (UTC) -
-{% endblock %} \ No newline at end of file + + {% if analysis %} + +
+
+
+
+
+
+

{{ analysis.symbol }}

+

{{ analysis.latest_date }}

+
+
+

LATEST PRICE

+

${{ analysis.latest_price }}

+
+
+

MARKOV PREDICTION

+

+ {{ analysis.predicted_state }} +

+

Probability: {{ analysis.probability }}

+
+
+

CURRENT STATE

+

{{ analysis.current_state }}

+
+
+

TREND DIRECTION

+

+ {{ analysis.trend }} +

+

SMA20: {{ analysis.sma20 }} | SMA50: {{ analysis.sma50 }}

+
+
+
+
+
+
+ +
+ +
+
+
+
Markov Transition Matrix
+
+
+
+ + + + + {% for name in analysis.state_names %} + + {% endfor %} + + + + {% for row in analysis.matrix %} + + + {% for prob in row %} + + {% endfor %} + + {% endfor %} + +
From \ To{{ forloop.counter0 }}
{{ forloop.counter0 }}{{ prob|floatformat:2 }}
+
+
+
Bin Definitions (Standard Deviation)
+
+
0 Very Big Drop (≤ -2σ)
+
1 Big Drop (-2σ to -1σ)
+
2 Small Drop (-1σ to mean)
+
3 Small Rise (mean to +1σ)
+
4 Big Rise (+1σ to +2σ)
+
5 Very Big Rise (> +2σ)
+
+
+
+
+
+ + +
+
+
+
SMA 20/50 Crossovers
+
+
+
+ + + + + + + + + + {% for cross in analysis.crossovers %} + + + + + + {% empty %} + + + + {% endfor %} + +
DatePriceEvent
{{ cross.Date }}{{ cross.Price }} + + {{ cross.Type }} + +
No recent crossovers detected.
+
+
+
+
+
+ + +
+
+
+
+
Interactive Candlestick Chart (100d)
+
+
+
+
+
+
+
+ {% endif %} + + {% if messages %} +
+
+ {% for message in messages %} + + {% endfor %} +
+
+ {% endif %} + +
+ + + +{% endblock %} diff --git a/core/urls.py b/core/urls.py index 6299e3d..34bd639 100644 --- a/core/urls.py +++ b/core/urls.py @@ -1,7 +1,6 @@ from django.urls import path - -from .views import home +from .views import index urlpatterns = [ - path("", home, name="home"), -] + path("", index, name="index"), +] \ No newline at end of file diff --git a/core/views.py b/core/views.py index c9aed12..c2a57f5 100644 --- a/core/views.py +++ b/core/views.py @@ -1,25 +1,34 @@ -import os -import platform - -from django import get_version as django_version from django.shortcuts import render -from django.utils import timezone +from django.contrib import messages +from .analysis import get_analysis_data +import json - -def home(request): - """Render the landing screen with loader and environment details.""" - host_name = request.get_host().lower() - agent_brand = "AppWizzy" if host_name == "appwizzy.com" else "Flatlogic" - now = timezone.now() - - context = { - "project_name": "New Style", - "agent_brand": agent_brand, - "django_version": django_version(), - "python_version": platform.python_version(), - "current_time": now, - "host_name": host_name, - "project_description": os.getenv("PROJECT_DESCRIPTION", ""), - "project_image_url": os.getenv("PROJECT_IMAGE_URL", ""), - } - return render(request, "core/index.html", context) +def index(request): + symbol = request.POST.get('symbol', '').strip().upper() + analysis = None + + # Store history in session + history = request.session.get('analysis_history', []) + + if request.method == 'POST' and symbol: + data, error = get_analysis_data(symbol) + if error: + messages.error(request, f"Error: {error}") + else: + analysis = data + # Update history + if symbol not in history: + history.insert(0, symbol) + request.session['analysis_history'] = history[:10] # Keep last 10 + + # Prepare chart JSON if analysis exists + chart_json = None + if analysis: + chart_json = json.dumps(analysis['chart_data']) + + return render(request, 'core/index.html', { + 'analysis': analysis, + 'symbol': symbol, + 'history': history, + 'chart_json': chart_json + }) \ No newline at end of file