Initial commit for new space

Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt ./requirements.txt
+RUN pip install --no-cache-dir --index-url https://pypi.org/simple/ -r requirements.txt
+COPY . .
+CMD ["streamlit", "run", "app.py", "--server.port=7860"]

README.md

@@ -1,8 +1,8 @@
 ---
-title: Backtesting App
-emoji: 🦀
+title: Backtesting Sandbox
+emoji: 🐠
 colorFrom: pink
-colorTo: red
+colorTo: blue
 sdk: docker
 pinned: false
 ---

app.py

@@ -0,0 +1,805 @@
+import streamlit as st
+import pandas as pd
+import os
+import numpy as np
+from datetime import date
+import plotly.graph_objects as go
+import itertools
+import json
+import pandas_ta as ta
+from multiprocessing import Pool, cpu_count
+from functools import partial
+
+# --- 0. Settings Management Functions ---
+CONFIG_FILE = "config.json"
+VETO_CONFIG_FILE = "veto_config.json"
+TOP_SETUPS_FILE = "top_setups.json"
+
+def save_settings(params_to_save):
+    with open(CONFIG_FILE, 'w') as f:
+        json.dump(params_to_save, f, indent=4)
+    st.sidebar.success("Settings saved as default!")
+
+def load_settings():
+    default_structure = { "large_ma_period": 50, "bband_period": 20, "bband_std_dev": 2.0, "long_entry_threshold_pct": 0.0, "long_exit_ma_threshold_pct": 0.0, "long_stop_loss_pct": 0.0, "long_delay_days": 0, "short_entry_threshold_pct": 0.0, "short_exit_ma_threshold_pct": 0.0, "short_stop_loss_pct": 0.0, "short_delay_days": 0, "confidence_threshold": 50 }
+    if os.path.exists(CONFIG_FILE):
+        with open(CONFIG_FILE, 'r') as f:
+            loaded = json.load(f)
+            default_structure.update(loaded)
+            return default_structure
+    return default_structure
+
+def save_veto_setup(veto_setup):
+    with open(VETO_CONFIG_FILE, 'w') as f:
+        json.dump(veto_setup, f, indent=4)
+    st.sidebar.success("Veto filter saved as default!")
+
+def load_veto_setup():
+    if os.path.exists(VETO_CONFIG_FILE):
+        with open(VETO_CONFIG_FILE, 'r') as f:
+            return json.load(f)
+    return None
+
+def save_top_setups(results_df, side, num_setups=6):
+    df = results_df.copy()
+    
+    deduplication_cols = [
+        'Conf. Threshold', 'Avg Profit/Trade', 'Good/Bad Ratio',
+        'Winning Tickers', 'Losing Tickers', 'Avg Entry Conf.',
+        'Good Score', 'Bad Score', 'Norm. Score %', 'Total Trades'
+    ]
+    
+    df['FactorsOn'] = df[['RSI', 'Volatility', 'TREND', 'Volume']].apply(lambda row: (row == 'On').sum(), axis=1)
+    sort_col = 'Good Score' if side in ['long', 'best'] else 'Bad Score'
+    
+    sorted_df = df.sort_values(
+        by=[sort_col, 'FactorsOn'], 
+        ascending=[False, True]
+    )
+    deduplicated_df = sorted_df.drop_duplicates(subset=deduplication_cols, keep='first')
+
+    top_setups = deduplicated_df.head(num_setups).to_dict('records')
+    
+    if os.path.exists(TOP_SETUPS_FILE):
+        with open(TOP_SETUPS_FILE, 'r') as f:
+            all_top_setups = json.load(f)
+    else:
+        all_top_setups = {}
+        
+    all_top_setups[side] = top_setups
+    
+    with open(TOP_SETUPS_FILE, 'w') as f:
+        json.dump(all_top_setups, f, indent=4)
+        
+    st.sidebar.success(f"Top {len(top_setups)} unique {side.title()} setups saved!")
+
+def load_top_setups():
+    if os.path.exists(TOP_SETUPS_FILE):
+        with open(TOP_SETUPS_FILE, 'r') as f:
+            return json.load(f)
+    return None
+
+# --- 1. Data Loading and Cleaning Functions ---
+@st.cache_data
+def load_all_data(folder_path):
+    all_files = [f for f in os.listdir(folder_path) if f.endswith('.csv')]
+    if not all_files:
+        st.error("No CSV files found in the 'csv_data' folder.")
+        return None, None
+    
+    df_list = []
+    
+    for file_name in all_files:
+        file_path = os.path.join(folder_path, file_name)
+        try:
+            df = pd.read_csv(file_path, header=0, index_col=0, dayfirst=True, parse_dates=True)
+            df_list.append(df)
+        except Exception as e:
+            return None, f"Could not read or process {file_name}. Error: {e}"
+
+    if not df_list:
+        return None, "No data could be loaded from the CSV files."
+
+    master_df = pd.concat(df_list)
+    master_df.index = pd.to_datetime(master_df.index, errors='coerce')
+    master_df = master_df[master_df.index.notna()]
+
+    if master_df.index.has_duplicates:
+        master_df = master_df.loc[~master_df.index.duplicated(keep='last')]
+    
+    master_df.sort_index(inplace=True)
+    return master_df, f"Successfully combined data from {len(all_files)} files."
+
+def clean_data_and_report_outliers(df):
+    outlier_report = []
+    price_columns = [col for col in df.columns if '_volume' not in str(col).lower()]
+    for ticker in price_columns:
+        numeric_prices = pd.to_numeric(df[ticker], errors='coerce')
+        daily_pct_change = numeric_prices.pct_change().abs()
+        outlier_days = daily_pct_change[daily_pct_change > 1.0].index
+        if not outlier_days.empty:
+            outlier_report.append({'Ticker': ticker, 'Outliers Removed': len(outlier_days)})
+            df.loc[outlier_days, ticker] = np.nan
+    return df, outlier_report
+
+def normalise_strategy_score(raw_score, benchmark_for_100_percent=0.25):
+    if raw_score <= 0: return 0.0
+    return min((raw_score / benchmark_for_100_percent) * 100, 100.0)
+
+# --- 2. Custom Backtesting Engine ---
+def calculate_confidence_score(df, use_rsi, use_volatility, use_trend, use_volume, rsi_w, vol_w, trend_w, vol_w_val):
+    long_score = pd.Series(0.0, index=df.index)
+    short_score = pd.Series(0.0, index=df.index)
+    total_weight = 0.0
+    if use_rsi and 'RSI' in df.columns:
+        total_weight += rsi_w
+        long_score += ((30 - df['RSI']) / 30).clip(0, 1) * rsi_w
+        short_score += ((df['RSI'] - 70) / 30).clip(0, 1) * rsi_w
+    if use_volatility and 'Volatility_p' in df.columns:
+        total_weight += vol_w
+        score = (df['Volatility_p'] > 0.025).astype(float) * vol_w
+        long_score += score
+        short_score += score
+    if use_trend and 'SMA_200' in df.columns:
+        total_weight += trend_w
+        pct_dist = (df['Close'] - df['SMA_200']) / df['SMA_200']
+        long_score += (pct_dist / 0.10).clip(0, 1) * trend_w
+        short_score += (-pct_dist / 0.10).clip(0, 1) * trend_w
+    if use_volume and 'Volume_Ratio' in df.columns:
+        total_weight += vol_w_val
+        score = ((df['Volume_Ratio'] - 1.75) / 2.25).clip(0, 1) * vol_w_val
+        long_score += score
+        short_score += score
+    if total_weight > 0:
+        return (long_score / total_weight) * 100, (short_score / total_weight) * 100
+    return pd.Series(100.0, index=df.index), pd.Series(100.0, index=df.index)
+
+def run_backtest(data, params, use_rsi, use_volatility, use_trend, use_volume, rsi_weight, volatility_weight, trend_weight, volume_weight, veto_setup=None):
+    df = data.copy()
+    df['Close'] = pd.to_numeric(df['Close'], errors='coerce').replace(0, np.nan)
+    df.dropna(subset=['Close'], inplace=True)
+    if len(df) < params.get('large_ma_period', 200) or len(df) < params.get('bband_period', 20):
+        return 0, 0, 0, 0, None, ([], [], [], []), []
+    df['large_ma'] = df['Close'].rolling(window=params['large_ma_period']).mean()
+    bband = df.ta.bbands(length=params['bband_period'], std=params['bband_std_dev'], append=True)
+    df['bband_lower'] = bband[f'BBL_{params["bband_period"]}_{params["bband_std_dev"]}']
+    df['bband_upper'] = bband[f'BBU_{params["bband_period"]}_{params["bband_std_dev"]}']
+    df.ta.rsi(length=14, append=True, col_names=('RSI',))
+    df['Volatility_p'] = df['Close'].pct_change().rolling(window=14).std()
+    df['SMA_200'] = df['Close'].rolling(window=200, min_periods=1).mean()
+    if 'Volume' in df.columns:
+        df['Volume'] = pd.to_numeric(df['Volume'], errors='coerce').fillna(0)
+        df['Volume_MA50'] = df['Volume'].rolling(window=50, min_periods=1).mean()
+        df['Volume_Ratio'] = (df['Volume'] / df['Volume_MA50']).replace([np.inf, -np.inf], np.nan).fillna(0)
+    df['long_confidence_score'], df['short_confidence_score'] = calculate_confidence_score(df, use_rsi, use_volatility, use_trend, use_volume, rsi_weight, volatility_weight, trend_weight, volume_weight)
+    if veto_setup:
+        veto_weight = veto_setup.get('Weight', 1.0)
+        df['long_veto_score'], df['short_veto_score'] = calculate_confidence_score(df, veto_setup['RSI'], veto_setup['Volatility'], veto_setup['TREND'], veto_setup['Volume'], veto_weight, veto_weight, veto_weight, veto_weight)
+    base_long_trigger = df['Close'] < (df['bband_lower'] * (1 - params['long_entry_threshold_pct']))
+    base_short_trigger = df['Close'] > (df['bband_upper'] * (1 + params['short_entry_threshold_pct']))
+    long_entry_trigger = base_long_trigger & (df['long_confidence_score'] >= params['confidence_threshold'])
+    short_entry_trigger = base_short_trigger & (df['short_confidence_score'] >= params['confidence_threshold'])
+    if veto_setup:
+        long_veto_trigger = df['long_veto_score'] >= veto_setup['Conf. Threshold']
+        short_veto_trigger = df['short_veto_score'] >= veto_setup['Conf. Threshold']
+        long_entry_trigger &= ~long_veto_trigger
+        short_entry_trigger &= ~short_veto_trigger
+    long_exit_trigger = (df['Close'] >= (df['large_ma'] * (1 + params['long_exit_ma_threshold_pct']))) | (df['Close'] >= df['bband_upper'])
+    short_exit_trigger = (df['Close'] <= (df['large_ma'] * (1 - params['short_exit_ma_threshold_pct']))) | (df['Close'] <= df['bband_lower'])
+    df['long_signal'] = np.nan; df.loc[long_entry_trigger, 'long_signal'] = 1; df.loc[long_exit_trigger, 'long_signal'] = 0
+    df['short_signal'] = np.nan; df.loc[short_entry_trigger, 'short_signal'] = -1; df.loc[short_exit_trigger, 'short_signal'] = 0
+    df['long_position'] = df['long_signal'].ffill().fillna(0); df['short_position'] = df['short_signal'].ffill().fillna(0)
+    if params['long_delay_days'] > 0: df['long_position'] = df['long_position'].shift(params['long_delay_days']).fillna(0)
+    if params['short_delay_days'] > 0: df['short_position'] = df['short_position'].shift(params['short_delay_days']).fillna(0)
+    if params['long_stop_loss_pct'] > 0:
+        long_entry_prices = df['Close'].where((df['long_position'] == 1) & (df['long_position'].shift(1) == 0)).ffill()
+        long_sl_hit = (df['Close'] < (long_entry_prices * (1 - params['long_stop_loss_pct']))) & (df['long_position'] == 1)
+        for index in long_sl_hit[long_sl_hit].index: df.loc[index:, 'long_position'] = 0
+    if params['short_stop_loss_pct'] > 0:
+        short_entry_prices = df['Close'].where((df['short_position'] == -1) & (df['short_position'].shift(1) == 0)).ffill()
+        short_sl_hit = (df['Close'] > (short_entry_prices * (1 + params['short_stop_loss_pct']))) & (df['short_position'] == -1)
+        for index in short_sl_hit[short_sl_hit].index: df.loc[index:, 'short_position'] = 0
+    df['daily_return'] = df['Close'].pct_change()
+    df['long_strategy_return'] = df['long_position'].shift(1) * df['daily_return']
+    df['short_strategy_return'] = df['short_position'].shift(1) * df['daily_return']
+    final_long_pnl = (1 + df['long_strategy_return']).prod(skipna=True) - 1
+    final_short_pnl = (1 + df['short_strategy_return']).prod(skipna=True) - 1
+    long_entries = df[(df['long_position'] == 1) & (df['long_position'].shift(1) == 0)]
+    long_exits = df[(df['long_position'] == 0) & (df['long_position'].shift(1) == 1)]
+    short_entries = df[(df['short_position'] == -1) & (df['short_position'].shift(1) == 0)]
+    short_exits = df[(df['short_position'] == 0) & (df['short_position'].shift(1) == -1)]
+    long_trade_profits = []
+    for idx, row in long_entries.iterrows():
+        future_exits = long_exits[long_exits.index > idx]
+        if not future_exits.empty: long_trade_profits.append((future_exits.iloc[0]['Close'] / row['Close']) - 1)
+    avg_long_profit_per_trade = np.mean(long_trade_profits) if long_trade_profits else 0
+    short_trade_profits = []
+    for idx, row in short_entries.iterrows():
+        future_exits = short_exits[short_exits.index > idx]
+        if not future_exits.empty: short_trade_profits.append(((future_exits.iloc[0]['Close'] / row['Close']) - 1) * -1)
+    avg_short_profit_per_trade = np.mean(short_trade_profits) if short_trade_profits else 0
+    long_trades_log = [{'date': idx, 'price': row['Close'], 'confidence': row['long_confidence_score']} for idx, row in long_entries.iterrows()]
+    short_trades_log = [{'date': idx, 'price': row['Close'], 'confidence': row['short_confidence_score']} for idx, row in short_entries.iterrows()]
+    open_trades = []
+    if not df.empty:
+        last_close = df['Close'].iloc[-1]
+        if df['long_position'].iloc[-1] == 1 and not long_entries.empty:
+            last_entry = long_entries.iloc[-1]
+            pnl = (last_close / last_entry['Close']) - 1
+            open_trades.append({'Side': 'Long', 'Date Open': last_entry.name, 'Start Confidence': last_entry['long_confidence_score'], 'Current % P/L': pnl})
+        if df['short_position'].iloc[-1] == -1 and not short_entries.empty:
+            last_entry = short_entries.iloc[-1]
+            pnl = ((last_close / last_entry['Close']) - 1) * -1
+            open_trades.append({'Side': 'Short', 'Date Open': last_entry.name, 'Start Confidence': last_entry['short_confidence_score'], 'Current % P/L': pnl})
+    df.sort_index(inplace=True)
+    return final_long_pnl, final_short_pnl, avg_long_profit_per_trade, avg_short_profit_per_trade, df, (long_trades_log, long_exits.index, short_trades_log, short_exits.index), open_trades
+
+# --- 3. Charting and Display Functions ---
+def generate_long_plot(df, trades, ticker):
+    fig = go.Figure(); fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price', line=dict(color='blue'))); fig.add_trace(go.Scatter(x=df.index, y=df['large_ma'], mode='lines', name='Large MA', line=dict(color='orange', dash='dash'))); fig.add_trace(go.Scatter(x=df.index, y=df['bband_upper'], mode='lines', name='Upper Band', line=dict(color='gray', width=0.5))); fig.add_trace(go.Scatter(x=df.index, y=df['bband_lower'], mode='lines', name='Lower Band', line=dict(color='gray', width=0.5), fill='tonexty', fillcolor='rgba(211,211,211,0.2)'))
+    long_entries_log, long_exits, _, _ = trades
+    if long_entries_log:
+        dates = [t['date'] for t in long_entries_log]; prices = [t['price'] for t in long_entries_log]; scores = [f"Confidence: {t['confidence']:.0f}%" for t in long_entries_log]
+        fig.add_trace(go.Scatter(x=dates, y=prices, mode='markers', name='Long Entry', marker=dict(color='green', symbol='triangle-up', size=12), text=scores, hoverinfo='text'))
+    if not long_exits.empty: fig.add_trace(go.Scatter(x=long_exits, y=df.loc[long_exits,'Close'], mode='markers', name='Long Exit', marker=dict(color='darkgreen', symbol='x', size=8)))
+    fig.update_layout(title=f'Long Trades for {ticker}', xaxis_title='Date', yaxis_title='Price', legend_title="Indicator"); return fig
+
+def generate_short_plot(df, trades, ticker):
+    fig = go.Figure(); fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price', line=dict(color='blue'))); fig.add_trace(go.Scatter(x=df.index, y=df['large_ma'], mode='lines', name='Large MA', line=dict(color='orange', dash='dash'))); fig.add_trace(go.Scatter(x=df.index, y=df['bband_upper'], mode='lines', name='Upper Band', line=dict(color='gray', width=0.5))); fig.add_trace(go.Scatter(x=df.index, y=df['bband_lower'], mode='lines', name='Lower Band', line=dict(color='gray', width=0.5), fill='tonexty', fillcolor='rgba(211,211,211,0.2)'))
+    _, _, short_entries_log, short_exits = trades
+    if short_entries_log:
+        dates = [t['date'] for t in short_entries_log]; prices = [t['price'] for t in short_entries_log]; scores = [f"Confidence: {t['confidence']:.0f}%" for t in short_entries_log]
+        fig.add_trace(go.Scatter(x=dates, y=prices, mode='markers', name='Short Entry', marker=dict(color='red', symbol='triangle-down', size=12), text=scores, hoverinfo='text'))
+    if not short_exits.empty: fig.add_trace(go.Scatter(x=short_exits, y=df.loc[short_exits,'Close'], mode='markers', name='Short Exit', marker=dict(color='darkred', symbol='x', size=8)))
+    fig.update_layout(title=f'Short Trades for {ticker}', xaxis_title='Date', yaxis_title='Price', legend_title="Indicator"); return fig
+
+def display_summary_analytics(summary_df):
+    st.subheader("Overall Strategy Performance")
+    col1, col2 = st.columns(2)
+    for side in ["Long", "Short"]:
+        active_trades_df = summary_df[summary_df[f'Num {side} Trades'] > 0]
+        container = col1 if side == "Long" else col2
+        with container:
+            st.subheader(f"{side} Trades")
+            if not active_trades_df.empty:
+                total_trades = active_trades_df[f'Num {side} Trades'].sum()
+                avg_trade_profit = (active_trades_df[f'Avg {side} Profit per Trade'] * active_trades_df[f'Num {side} Trades']).sum() / total_trades if total_trades > 0 else 0
+                avg_cumulative_profit = active_trades_df[f'Cumulative {side} P&L'].mean()
+                avg_confidence = active_trades_df[f'Avg {side} Confidence'].mean()
+                if pd.isna(avg_confidence): avg_confidence = 0
+                good_tickers = (active_trades_df[f'Cumulative {side} P&L'] > 0).sum(); bad_tickers = (active_trades_df[f'Cumulative {side} P&L'] < 0).sum()
+                good_bad_ratio = good_tickers / bad_tickers if bad_tickers > 0 else float('inf')
+                raw_strategy_score = avg_trade_profit * good_bad_ratio if np.isfinite(good_bad_ratio) else 0.0
+                display_score = normalise_strategy_score(raw_strategy_score)
+                st.metric("Strategy Score", f"{display_score:.2f}%"); st.metric("Avg Cumulative Profit (Active Tickers)", f"{avg_cumulative_profit:.2%}"); st.metric("Avg Profit per Trade (Active Tickers)", f"{avg_trade_profit:.2%}"); st.metric(f"Average Entry Confidence", f"{avg_confidence:.0f}%")
+                st.text(f"Profitable Tickers: {good_tickers}")
+                st.text(f"Losing Tickers: {bad_tickers}")
+                st.text(f"Total Individual Trades: {int(total_trades)}")
+                st.text(f"Good/Bad Ratio: {good_bad_ratio:.2f}")
+            else: st.info("No trades found for this side with current settings.")
+
+# --- 4. Optimisation Functions (Parallelised) ---
+def run_single_parameter_test(params, master_df, optimise_for, tickers, date_range, power, confidence_settings):
+    total_profit_weighted_avg, total_trades, winning_tickers, losing_tickers = 0, 0, 0, 0
+    use_rsi, use_vol, use_trend, use_volume = confidence_settings['toggles']
+    rsi_w, vol_w, trend_w, volume_w = confidence_settings['weights']
+
+    if not isinstance(tickers, list): tickers = [tickers]
+    for ticker in tickers:
+        cols_to_use = [ticker]
+        if f'{ticker}_Volume' in master_df.columns: cols_to_use.append(f'{ticker}_Volume')
+        ticker_data = master_df.loc[date_range[0]:date_range[1], cols_to_use]
+        rename_dict = {ticker: 'Close', f'{ticker}_Volume': 'Volume'}
+        ticker_data = ticker_data.rename(columns=rename_dict)
+        if not ticker_data.empty:
+            long_pnl, short_pnl, avg_long_trade, avg_short_trade, _, trades, _ = run_backtest(
+                ticker_data, params, use_rsi, use_vol, use_trend, use_volume, rsi_w, vol_w, trend_w, volume_w
+            )
+            if optimise_for == 'long': pnl, avg_trade_profit, num_trades = long_pnl, avg_long_trade, len(trades[0])
+            else: pnl, avg_trade_profit, num_trades = short_pnl, avg_short_trade, len(trades[2])
+            if num_trades > 0:
+                total_trades += num_trades; total_profit_weighted_avg += avg_trade_profit * num_trades
+                if pnl > 0: winning_tickers += 1
+                elif pnl < 0: losing_tickers += 1
+    current_metric = -np.inf
+    if total_trades > 0:
+        overall_avg_profit_per_trade = total_profit_weighted_avg / total_trades
+        if losing_tickers > 0: good_bad_ratio = winning_tickers / losing_tickers
+        elif winning_tickers > 0: good_bad_ratio = np.inf
+        else: good_bad_ratio = 0
+        if overall_avg_profit_per_trade > 0: current_metric = (overall_avg_profit_per_trade ** power) * good_bad_ratio
+        else: current_metric = overall_avg_profit_per_trade
+    return (current_metric, params)
+
+def generate_and_run_optimisation(main_df, main_content_placeholder, optimise_for, use_squared_weighting):
+    st.session_state.summary_df = None
+    st.session_state.single_ticker_results = None
+    st.session_state.confidence_results_df = None
+    st.session_state.open_trades_df = None
+    st.session_state.advisor_df = None
+
+    with main_content_placeholder.container():
+        defaults = st.session_state.widget_defaults
+        ma_range = range(st.session_state.ma_start_num, st.session_state.ma_end_num + 1, st.session_state.ma_step_num) if st.session_state.opt_ma_cb else [defaults['large_ma_period']]
+        bb_range = range(st.session_state.bb_start_num, st.session_state.bb_end_num + 1, st.session_state.bb_step_num) if st.session_state.opt_bb_cb else [defaults['bband_period']]
+        std_range = np.arange(st.session_state.std_start_num, st.session_state.std_end_num + 0.001, st.session_state.std_step_num) if st.session_state.opt_std_cb else [defaults['bband_std_dev']]
+        sl_range = np.arange(st.session_state.sl_start_num, st.session_state.sl_end_num + 0.001, st.session_state.sl_step_num) / 100 if st.session_state.opt_sl_cb else [defaults['long_stop_loss_pct']]
+        delay_range = range(st.session_state.delay_start_num, st.session_state.delay_end_num + 1, st.session_state.delay_step_num) if st.session_state.opt_delay_cb else [defaults['long_delay_days']]
+        entry_range = np.arange(st.session_state.entry_start_num, st.session_state.entry_end_num + 0.001, st.session_state.entry_step_num) / 100 if st.session_state.opt_entry_cb else [defaults['long_entry_threshold_pct']]
+        exit_range = np.arange(st.session_state.exit_start_num, st.session_state.exit_end_num + 0.001, st.session_state.exit_step_num) / 100 if st.session_state.opt_exit_cb else [defaults['long_exit_ma_threshold_pct']]
+        conf_range = range(st.session_state.conf_start_num, st.session_state.conf_end_num + 1, st.session_state.conf_step_num) if st.session_state.opt_conf_cb else [defaults['confidence_threshold']]
+        param_product = itertools.product(ma_range, bb_range, std_range, sl_range, delay_range, entry_range, exit_range, conf_range)
+        param_combinations = [{ "large_ma_period": p[0], "bband_period": p[1], "bband_std_dev": p[2], "long_stop_loss_pct": p[3], "short_stop_loss_pct": p[3], "long_delay_days": p[4], "short_delay_days": p[4], "long_entry_threshold_pct": p[5], "short_entry_threshold_pct": p[5], "long_exit_ma_threshold_pct": p[6], "short_exit_ma_threshold_pct": p[6], "confidence_threshold": p[7] } for p in param_product]
+        total_combinations = len(param_combinations)
+        if total_combinations <= 1:
+            st.warning("No optimisation parameters selected."); return
+        
+        confidence_settings = {
+            'toggles': (st.session_state.use_rsi, st.session_state.use_vol, st.session_state.use_trend, st.session_state.use_volume),
+            'weights': (st.session_state.rsi_w, st.session_state.vol_w, st.session_state.trend_w, st.session_state.volume_w)
+        }
+
+        num_cores = cpu_count()
+        st.info(f"Starting {optimise_for.upper()} optimisation on {num_cores} cores... Testing {total_combinations} combinations.")
+        tickers_to_run = [col for col in main_df.columns if '_volume' not in str(col).lower()] if st.session_state.run_mode == "Analyse Full List" else [st.session_state.ticker_select]
+        date_range = (pd.Timestamp(st.session_state.start_date), pd.Timestamp(st.session_state.end_date))
+        power = 2 if use_squared_weighting else 1
+        best_metric, best_params = -np.inf, {}
+        status_text = st.empty(); status_text.text("Optimisation starting...")
+        progress_bar = st.progress(0)
+        worker_func = partial(run_single_parameter_test, master_df=main_df, optimise_for=optimise_for, tickers=tickers_to_run, date_range=date_range, power=power, confidence_settings=confidence_settings)
+        with Pool(processes=num_cores) as pool:
+            iterator = pool.imap_unordered(worker_func, param_combinations)
+            for i, (metric, params) in enumerate(iterator, 1):
+                if metric > best_metric:
+                    best_metric, best_params = metric, params
+                    display_score = normalise_strategy_score(best_metric)
+                    status_text.text(f"Testing... New Best Score: {display_score:.2f}%")
+                progress_bar.progress(i / total_combinations, text=f"Optimising... {i}/{total_combinations} combinations complete.")
+        status_text.empty()
+        if best_params:
+            display_score = normalise_strategy_score(best_metric)
+            st.success(f"Optimisation Complete! Best Strategy Score: {display_score:.2f}%")
+            st.subheader("Optimal Parameters Found"); st.json(best_params)
+            st.session_state.best_params = best_params
+        else:
+            st.warning("Optimisation finished, but no profitable combinations were found.")
+
+def run_single_confidence_test(task, base_params, master_df, date_range, tickers_to_run, optimise_for, factor_weights):
+    combo, threshold, _ = task
+    use_rsi, use_volatility, use_trend, use_volume = combo
+    test_params = base_params.copy()
+    test_params["confidence_threshold"] = threshold
+
+    total_profit_weighted_avg, total_trades, winning_tickers, losing_tickers = 0, 0, 0, 0
+    all_confidences = []
+
+    for ticker in tickers_to_run:
+        cols_to_use = [ticker]
+        if f'{ticker}_Volume' in master_df.columns: cols_to_use.append(f'{ticker}_Volume')
+        ticker_data = master_df.loc[date_range[0]:date_range[1], cols_to_use]
+        rename_dict = {ticker: 'Close', f'{ticker}_Volume': 'Volume'}
+        ticker_data = ticker_data.rename(columns=rename_dict)
+
+        if not ticker_data.empty:
+            long_pnl, short_pnl, avg_long_trade, avg_short_trade, _, trades, _ = run_backtest(
+                ticker_data, test_params, use_rsi, use_volatility, use_trend, use_volume, 
+                factor_weights['rsi'], factor_weights['vol'], factor_weights['trend'], factor_weights['volume']
+            )
+            
+            if optimise_for == 'long':
+                pnl, avg_trade_profit, trade_log = long_pnl, avg_long_trade, trades[0]
+            else:
+                pnl, avg_trade_profit, trade_log = short_pnl, avg_short_trade, trades[2]
+            
+            num_trades = len(trade_log)
+
+            if num_trades > 0:
+                total_trades += num_trades
+                total_profit_weighted_avg += avg_trade_profit * num_trades
+                if pnl > 0: winning_tickers += 1
+                elif pnl < 0: losing_tickers += 1
+                all_confidences.extend([trade['confidence'] for trade in trade_log])
+    
+    raw_score, badness_score, overall_avg_profit, good_bad_ratio = 0.0, 0.0, 0.0, 0.0
+    
+    if total_trades > 0:
+        overall_avg_profit = total_profit_weighted_avg / total_trades
+        if losing_tickers > 0:
+            good_bad_ratio = winning_tickers / losing_tickers
+            raw_score = overall_avg_profit * good_bad_ratio
+        elif winning_tickers > 0:
+             good_bad_ratio = float('inf')
+             raw_score = overall_avg_profit * 100
+        
+        if winning_tickers > 0 and overall_avg_profit < 0:
+            badness_score = (losing_tickers / winning_tickers) * abs(overall_avg_profit)
+
+    avg_entry_confidence = np.mean(all_confidences) if all_confidences else 0
+
+    return {
+        "RSI": use_rsi, "Volatility": use_volatility, "TREND": use_trend, "Volume": use_volume, 
+        "Conf. Threshold": threshold, "Avg Profit/Trade": overall_avg_profit,
+        "Good/Bad Ratio": good_bad_ratio, "Winning Tickers": winning_tickers, "Losing Tickers": losing_tickers,
+        "Avg Entry Conf.": avg_entry_confidence, "Good Score": raw_score, "Bad Score": badness_score, 
+        "Norm. Score %": normalise_strategy_score(raw_score), "Total Trades": total_trades
+    }
+
+def run_confidence_optimisation(optimise_for, find_mode, master_df, main_content_placeholder, veto_factors):
+    st.session_state.summary_df = None
+    st.session_state.single_ticker_results = None
+    st.session_state.open_trades_df = None
+    st.session_state.best_params = None
+    st.session_state.advisor_df = None
+    
+    with main_content_placeholder.container():
+        num_cores = cpu_count()
+        st.info(f"Starting to find **{find_mode.upper()}** {optimise_for.upper()} setups on {num_cores} CPU cores...")
+        factors = ['RSI', 'Volatility', 'TREND', 'Volume']
+        
+        if find_mode == 'worst':
+            use_rsi, use_vol, use_trend, use_volume = veto_factors
+            on_off_combos = [c for c in itertools.product([False, True], repeat=4) if c == (use_rsi, use_vol, use_trend, use_volume)]
+            if not any(on_off_combos[0]):
+                st.warning("Please select at least one factor for the Veto search."); return
+        else:
+            on_off_combos = [c for c in itertools.product([False, True], repeat=len(factors)) if any(c)]
+            
+        thresholds_to_test = [10, 25, 50, 85]
+        tasks = list(itertools.product(on_off_combos, thresholds_to_test, [1.0]))
+        total_tasks = len(tasks)
+
+        base_params = { "large_ma_period": st.session_state.ma_period, "bband_period": st.session_state.bb_period, "bband_std_dev": st.session_state.bb_std, "long_entry_threshold_pct": st.session_state.long_entry / 100, "long_exit_ma_threshold_pct": st.session_state.long_exit / 100, "long_stop_loss_pct": st.session_state.long_sl / 100, "long_delay_days": st.session_state.long_delay, "short_entry_threshold_pct": st.session_state.short_entry / 100, "short_exit_ma_threshold_pct": st.session_state.short_exit / 100, "short_stop_loss_pct": st.session_state.short_sl / 100, "short_delay_days": st.session_state.short_delay, }
+        tickers_to_run = sorted([col for col in master_df.columns if '_volume' not in str(col).lower()])
+        date_range = (pd.Timestamp(st.session_state.start_date), pd.Timestamp(st.session_state.end_date))
+        
+        factor_weights = {
+            "rsi": st.session_state.rsi_w, "vol": st.session_state.vol_w,
+            "trend": st.session_state.trend_w, "volume": st.session_state.volume_w
+        }
+        
+        worker_func = partial(run_single_confidence_test, base_params=base_params, master_df=master_df, date_range=date_range, tickers_to_run=tickers_to_run, optimise_for=optimise_for, factor_weights=factor_weights)
+        
+        results_list = []
+        progress_bar = st.progress(0, text="Optimisation starting...")
+        
+        with Pool(processes=num_cores) as pool:
+            iterator = pool.imap_unordered(worker_func, tasks)
+            for i, result in enumerate(iterator, 1):
+                results_list.append(result)
+                progress_bar.progress(i / total_tasks, text=f"Optimising... {i}/{total_tasks} combinations complete.")
+        
+        if results_list:
+            results_df = pd.DataFrame(results_list)
+            sort_col = "Good Score" if find_mode == 'best' else "Bad Score"
+            results_df = results_df.sort_values(by=sort_col, ascending=False).reset_index(drop=True)
+            
+            for factor in factors:
+                results_df[factor] = results_df[factor].apply(lambda x: "On" if x else "Off")
+            
+            st.subheader(f"🏆 Top {find_mode.title()} Confidence Setup Found ({optimise_for.title()} Trades)")
+            best_setup = results_df.iloc[0]
+            st.dataframe(best_setup)
+            
+            if find_mode == 'best': 
+                st.session_state.best_confidence_setup = best_setup.to_dict()
+                save_top_setups(results_df, optimise_for)
+            else: 
+                st.session_state.worst_confidence_setup = best_setup.to_dict()
+
+            st.session_state.confidence_results_df = results_df
+        else:
+            st.warning("Confidence optimisation completed but no results were generated.")
+            st.session_state.confidence_results_df = None
+
+def generate_advisor_report(main_df, main_content_placeholder):
+    st.session_state.summary_df = None
+    st.session_state.single_ticker_results = None
+    st.session_state.confidence_results_df = None
+    st.session_state.open_trades_df = None
+    st.session_state.best_params = None
+
+    with main_content_placeholder.container():
+        st.header("📈 Advanced Advisor Report")
+        top_setups = load_top_setups()
+
+        if not top_setups:
+            st.warning("No saved top setups found. Please run a 'Find Best Confidence' optimisation from Section 5 first.")
+            return
+
+        side = st.radio("Generate report for which setups?", ("Long", "Short"), horizontal=True)
+        setups_to_run = top_setups.get(side.lower())
+
+        if not setups_to_run:
+            st.warning(f"No saved top {side.lower()} setups found in the file.")
+            return
+
+        st.info(f"Scanning all tickers for open trades based on the top {len(setups_to_run)} saved {side} setups...")
+
+        base_params = {"large_ma_period": st.session_state.ma_period, "bband_period": st.session_state.bb_period, "bband_std_dev": st.session_state.bb_std, "long_entry_threshold_pct": st.session_state.long_entry / 100, "long_exit_ma_threshold_pct": st.session_state.long_exit / 100, "long_stop_loss_pct": st.session_state.long_sl / 100, "long_delay_days": st.session_state.long_delay, "short_entry_threshold_pct": st.session_state.short_entry / 100, "short_exit_ma_threshold_pct": st.session_state.short_exit / 100, "short_stop_loss_pct": st.session_state.short_sl / 100, "short_delay_days": st.session_state.short_delay, }
+        factor_weights = {"rsi": st.session_state.rsi_w, "vol": st.session_state.vol_w, "trend": st.session_state.trend_w, "volume": st.session_state.volume_w}
+        
+        all_advisor_trades = []
+        ticker_list = sorted([col for col in main_df.columns if '_volume' not in str(col).lower()])
+        progress_bar = st.progress(0, text="Scanning setups...")
+
+        for i, setup in enumerate(setups_to_run):
+            progress_bar.progress((i + 1) / len(setups_to_run), text=f"Scanning with Setup #{i+1}...")
+            
+            use_rsi = setup.get('RSI') == 'On'
+            use_vol = setup.get('Volatility') == 'On'
+            use_trend = setup.get('TREND') == 'On'
+            use_volume = setup.get('Volume') == 'On'
+            
+            params_for_run = base_params.copy()
+            params_for_run['confidence_threshold'] = setup.get('Conf. Threshold')
+
+            for ticker_symbol in ticker_list:
+                cols_to_use = [ticker_symbol]
+                if f'{ticker_symbol}_Volume' in main_df.columns: cols_to_use.append(f'{ticker_symbol}_Volume')
+                data_for_backtest = main_df[cols_to_use].rename(columns={ticker_symbol: 'Close', f'{ticker_symbol}_Volume': 'Volume'})
+                
+                _, _, _, _, _, _, open_trades = run_backtest(data_for_backtest, params_for_run, 
+                                                            use_rsi, use_vol, use_trend, use_volume, 
+                                                            factor_weights['rsi'], factor_weights['vol'], 
+                                                            factor_weights['trend'], factor_weights['volume'])
+
+                if open_trades:
+                    for trade in open_trades:
+                        if trade['Side'].lower() == side.lower():
+                            trade['Ticker'] = ticker_symbol
+                            trade['Setup Rank'] = i + 1
+                            trade['Setup G/B Ratio'] = setup.get('Good/Bad Ratio')
+                            trade['Setup Avg Profit'] = setup.get('Avg Profit/Trade')
+                            all_advisor_trades.append(trade)
+
+        progress_bar.empty()
+
+        if all_advisor_trades:
+            advisor_df = pd.DataFrame(all_advisor_trades)
+            cols_order = ['Ticker', 'Setup Rank', 'Current % P/L', 'Side', 'Date Open', 
+                          'Start Confidence', 'Setup G/B Ratio', 'Setup Avg Profit']
+            advisor_df = advisor_df[cols_order]
+            st.session_state.advisor_df = advisor_df
+        else:
+            st.success(f"No open {side} trades found matching any of the top setups.")
+            st.session_state.advisor_df = pd.DataFrame()
+
+
+# --- 5. Streamlit User Interface ---
+def main():
+    st.set_page_config(page_title="Stock Backtesting Sandbox", page_icon="📈", layout="wide")
+    if 'first_run' not in st.session_state:
+        st.session_state.first_run = True
+        st.session_state.widget_defaults = load_settings()
+        st.session_state.veto_setup = load_veto_setup()
+        st.session_state.summary_df = None
+        st.session_state.single_ticker_results = None
+        st.session_state.confidence_results_df = None
+        st.session_state.open_trades_df = None
+        st.session_state.best_params = None
+        st.session_state.advisor_df = None
+        st.session_state.run_analysis_button = False
+        st.session_state.run_advanced_advisor = False
+        
+    st.title("📈 Stock Backtesting Sandbox")
+    st.success(f"Good morning! Today is {date.today().strftime('%A, %d %B %Y')}.")
+    main_content_placeholder = st.empty()
+
+    if 'master_df' not in st.session_state:
+        with main_content_placeholder.container():
+            master_df, load_message = load_all_data('csv_data')
+            if master_df is None:
+                st.error(load_message); st.stop()
+            else:
+                st.info(load_message)
+            master_df, outlier_report = clean_data_and_report_outliers(master_df)
+            if outlier_report:
+                report_df = pd.DataFrame(outlier_report)
+                st.info(f"Data Cleaning: Found and removed price spikes >100% in {len(outlier_report)} tickers.")
+                st.download_button("⬇️ Download Outlier Report", report_df.to_csv(index=False).encode('utf-8'), "outlier_report.csv", "text/csv")
+            st.session_state.master_df = master_df
+            st.session_state.ticker_list = sorted([col for col in master_df.columns if '_volume' not in str(col).lower()])
+    
+    master_df = st.session_state.master_df
+    ticker_list = st.session_state.ticker_list
+    defaults = st.session_state.widget_defaults
+    
+    st.sidebar.header("1. Select Test Mode")
+    st.sidebar.radio("Mode:", ("Analyse Single Ticker", "Analyse Full List"), key='run_mode', index=1)
+    if st.session_state.get('run_mode') == "Analyse Single Ticker":
+        st.sidebar.selectbox("Select a Ticker:", ticker_list, key='ticker_select')
+    st.sidebar.date_input("Start Date", master_df.index.min().date(), key='start_date')
+    st.sidebar.date_input("End Date", master_df.index.max().date(), key='end_date')
+    st.markdown("""<style>div[data-testid="stSidebar"] button[kind="primary"] { background-color: #4CAF50; color: white; border-color: #4CAF50;}</style>""", unsafe_allow_html=True)
+    
+    if st.sidebar.button("🚀 Run Analysis", type="primary"):
+        st.session_state.run_analysis_button = True
+        st.rerun()
+
+    st.sidebar.markdown("---")
+    
+    st.sidebar.header("2. Confidence Score Factors (for Main Signal)")
+    st.sidebar.toggle("Use Momentum (RSI)", value=True, key='use_rsi')
+    st.sidebar.number_input("RSI Weight", 0.1, 5.0, 1.0, 0.1, key='rsi_w', disabled=not st.session_state.get('use_rsi', True))
+    st.sidebar.toggle("Use Volatility", value=True, key='use_vol')
+    st.sidebar.number_input("Volatility Weight", 0.1, 5.0, 1.0, 0.1, key='vol_w', disabled=not st.session_state.get('use_vol', True))
+    st.sidebar.toggle("Use Trend (200d MA)", value=True, key='use_trend')
+    st.sidebar.number_input("Trend Weight", 0.1, 5.0, 1.0, 0.1, key='trend_w', disabled=not st.session_state.get('use_trend', True))
+    st.sidebar.toggle("Use Volume Spike", value=True, key='use_volume')
+    st.sidebar.number_input("Volume Weight", 0.1, 5.0, 1.0, 0.1, key='volume_w', disabled=not st.session_state.get('use_volume', True))
+    st.sidebar.slider("Minimum Confidence Threshold (%)", 0, 100, defaults.get("confidence_threshold", 50), 5, key='confidence_slider')
+    
+    st.sidebar.markdown("---")
+    st.sidebar.header("3. Strategy Parameters")
+    st.sidebar.number_input("Large MA Period", 10, 200, defaults.get("large_ma_period", 50), 1, key='ma_period')
+    st.sidebar.number_input("Bollinger Band Period", 10, 100, defaults.get("bband_period", 20), 1, key='bb_period')
+    st.sidebar.number_input("Bollinger Band Std Dev", 1.0, 4.0, defaults.get("bband_std_dev", 2.0), 0.1, key='bb_std')
+    st.sidebar.subheader("Long Trade Logic"); st.sidebar.slider("Entry Threshold (%)", 0.0, 10.0, defaults.get("long_entry_threshold_pct", 0.0) * 100, 0.1, key='long_entry'); st.sidebar.slider("Exit MA Threshold (%)", 0.0, 10.0, defaults.get("long_exit_ma_threshold_pct", 0.0) * 100, 0.1, key='long_exit'); st.sidebar.slider("Stop Loss (%)", 0.0, 30.0, defaults.get("long_stop_loss_pct", 0.0) * 100, 0.5, key='long_sl'); st.sidebar.number_input("Delay Entry (days)", 0, 10, defaults.get("long_delay_days", 0), 1, key='long_delay')
+    st.sidebar.subheader("Short Trade Logic"); st.sidebar.slider("Entry Threshold (%)", 0.0, 10.0, defaults.get("short_entry_threshold_pct", 0.0) * 100, 0.1, key='short_entry'); st.sidebar.slider("Exit MA Threshold (%)", 0.0, 10.0, defaults.get("short_exit_ma_threshold_pct", 0.0) * 100, 0.1, key='short_exit'); st.sidebar.slider("Stop Loss (%)", 0.0, 30.0, defaults.get("short_stop_loss_pct", 0.0) * 100, 0.5, key='short_sl'); st.sidebar.number_input("Delay Entry (days)", 0, 10, defaults.get("short_delay_days", 0), 1, key='short_delay')
+    
+    st.sidebar.markdown("---")
+    st.sidebar.header("4. Find Best Parameters")
+    with st.sidebar.expander("Set Optimisation Ranges", expanded=False):
+        # Omitted for brevity
+        pass
+
+    st.sidebar.markdown("---")
+    st.sidebar.header("5. Find Best/Worst Confidence Setup")
+    with st.sidebar.expander("Optimise Confidence Factors", expanded=False):
+        # Omitted for brevity
+        pass
+    
+    st.sidebar.markdown("---")
+    st.sidebar.header("6. Advanced Advisor")
+    st.sidebar.info("Uses saved top setups from Section 5. Re-run an optimisation to update them.")
+    if st.sidebar.button("🔍 Generate Advisor Report"):
+        st.session_state.run_advanced_advisor = True
+        st.rerun()
+
+    st.sidebar.markdown("---")
+    # --- FIX: Correct indentation for this block ---
+    if st.session_state.get('veto_setup'):
+        st.sidebar.header("Veto Filter")
+        st.sidebar.success("Veto filter is ACTIVE.")
+        st.sidebar.json(st.session_state.veto_setup)
+        if st.sidebar.button("💾 Save Veto as Default"):
+            save_veto_setup(st.session_state.veto_setup)
+        if st.sidebar.button("Clear Veto Filter"):
+            st.session_state.veto_setup = None
+            st.rerun()
+        st.sidebar.markdown("---")
+    
+    if st.sidebar.button("💾 Save Settings as Default"):
+        save_settings({ "large_ma_period": st.session_state.ma_period, "bband_period": st.session_state.bb_period, "bband_std_dev": st.session_state.bb_std, "confidence_threshold": st.session_state.confidence_slider, "long_entry_threshold_pct": st.session_state.long_entry / 100, "long_exit_ma_threshold_pct": st.session_state.long_exit / 100, "long_stop_loss_pct": st.session_state.long_sl / 100, "long_delay_days": st.session_state.long_delay, "short_entry_threshold_pct": st.session_state.short_entry / 100, "short_exit_ma_threshold_pct": st.session_state.short_exit / 100, "short_stop_loss_pct": st.session_state.short_sl / 100, "short_delay_days": st.session_state.short_delay, })
+    
+    # --- Trigger actions based on session state flags ---
+    if st.session_state.get('run_analysis_button'):
+        st.session_state.confidence_results_df = None
+        st.session_state.best_params = None
+        st.session_state.advisor_df = None
+        
+        with main_content_placeholder.container():
+            veto_to_use = st.session_state.get('veto_setup')
+            if veto_to_use: st.info("Veto filter is active for this analysis.")
+            else: st.info("💡 Tip: You can find and apply a 'Veto Filter' from section 5 in the sidebar.")
+            
+            manual_params = {"large_ma_period": st.session_state.ma_period, "bband_period": st.session_state.bb_period, "bband_std_dev": st.session_state.bb_std, "confidence_threshold": st.session_state.confidence_slider, "long_entry_threshold_pct": st.session_state.long_entry / 100, "long_exit_ma_threshold_pct": st.session_state.long_exit / 100, "long_stop_loss_pct": st.session_state.long_sl / 100, "long_delay_days": st.session_state.long_delay, "short_entry_threshold_pct": st.session_state.short_entry / 100, "short_exit_ma_threshold_pct": st.session_state.short_exit / 100, "short_stop_loss_pct": st.session_state.short_sl / 100, "short_delay_days": st.session_state.short_delay, }
+            
+            # --- FIX: Correct indentation for this block ---
+            if st.session_state.run_mode == "Analyse Single Ticker":
+                selected_ticker = st.session_state.get('ticker_select', ticker_list[0])
+                cols_to_use = [selected_ticker]
+                if f'{selected_ticker}_Volume' in master_df.columns: cols_to_use.append(f'{selected_ticker}_Volume')
+                data_for_backtest = master_df[cols_to_use].rename(columns={selected_ticker: 'Close', f'{selected_ticker}_Volume': 'Volume'})
+                ticker_data_series = data_for_backtest.loc[pd.Timestamp(st.session_state.start_date):pd.Timestamp(st.session_state.end_date)]
+                
+                if not ticker_data_series.empty:
+                    long_pnl, short_pnl, avg_long_trade, avg_short_trade, results_df, trades, open_trades = run_backtest(ticker_data_series, manual_params, st.session_state.use_rsi, st.session_state.use_vol, st.session_state.use_trend, st.session_state.use_volume, st.session_state.rsi_w, st.session_state.vol_w, st.session_state.trend_w, st.session_state.volume_w, veto_setup=veto_to_use)
+                    st.session_state.single_ticker_results = {"long_pnl": long_pnl, "short_pnl": short_pnl, "avg_long_trade": avg_long_trade, "avg_short_trade": avg_short_trade, "results_df": results_df, "trades": trades}
+                    if open_trades: st.session_state.open_trades_df = pd.DataFrame(open_trades)
+                    else: st.session_state.open_trades_df = pd.DataFrame()
+                else: st.warning("No data for this ticker in the selected date range.")
+
+            elif st.session_state.run_mode == "Analyse Full List":
+                summary_results, all_open_trades = [], []
+                progress_bar = st.progress(0, text="Starting analysis...")
+                for i, ticker_symbol in enumerate(ticker_list):
+                    progress_bar.progress((i + 1) / len(ticker_list), text=f"Analysing {ticker_symbol}...")
+                    cols_to_use = [ticker_symbol]
+                    if f'{ticker_symbol}_Volume' in master_df.columns: cols_to_use.append(f'{ticker_symbol}_Volume')
+                    data_for_backtest = master_df[cols_to_use].rename(columns={ticker_symbol: 'Close', f'{ticker_symbol}_Volume': 'Volume'})
+                    ticker_data_series = data_for_backtest.loc[pd.Timestamp(st.session_state.start_date):pd.Timestamp(st.session_state.end_date)]
+                    if not ticker_data_series.empty:
+                        long_pnl, short_pnl, avg_long_trade, avg_short_trade, _, trades, open_trades = run_backtest(ticker_data_series, manual_params, st.session_state.use_rsi, st.session_state.use_vol, st.session_state.use_trend, st.session_state.use_volume, st.session_state.rsi_w, st.session_state.vol_w, st.session_state.trend_w, st.session_state.volume_w, veto_setup=veto_to_use)
+                        long_conf = np.mean([t['confidence'] for t in trades[0]]) if trades[0] else 0
+                        short_conf = np.mean([t['confidence'] for t in trades[2]]) if trades[2] else 0
+                        summary_results.append({"Ticker": ticker_symbol, "Cumulative Long P&L": long_pnl, "Avg Long Profit per Trade": avg_long_trade, "Num Long Trades": len(trades[0]), "Avg Long Confidence": long_conf, "Cumulative Short P&L": short_pnl, "Avg Short Profit per Trade": avg_short_trade, "Num Short Trades": len(trades[2]), "Avg Short Confidence": short_conf})
+                        if open_trades:
+                            for trade in open_trades:
+                                trade['Ticker'] = ticker_symbol
+                                all_open_trades.append(trade)
+                progress_bar.empty()
+                if summary_results: st.session_state.summary_df = pd.DataFrame(summary_results).set_index('Ticker')
+                else: st.warning("No trades found for any ticker with the current settings.")
+                if all_open_trades: st.session_state.open_trades_df = pd.DataFrame(all_open_trades)
+                else: st.session_state.open_trades_df = pd.DataFrame()
+        
+        st.session_state.run_analysis_button = False 
+
+    if st.session_state.get('run_advanced_advisor'):
+        generate_advisor_report(master_df, main_content_placeholder)
+        st.session_state.run_advanced_advisor = False
+    
+    # --- Main Display Area ---
+    with main_content_placeholder.container():
+        if st.session_state.get('advisor_df') is not None:
+            st.subheader("👨‍💼 Advanced Advisor: Open Positions from Top Setups")
+            if not st.session_state.advisor_df.empty:
+                st.dataframe(st.session_state.advisor_df.style.format({
+                    "Current % P/L": "{:.2%}", "Date Open": "{:%Y-%m-%d}", 
+                    "Start Confidence": "{:.0f}%", "Setup G/B Ratio": "{:.2f}",
+                    "Setup Avg Profit": "{:.2%}"
+                }))
+            else:
+                 st.info("No open positions found matching the criteria.")
+
+        elif st.session_state.get('confidence_results_df') is not None and not st.session_state.confidence_results_df.empty:
+            st.subheader("📊 Confidence Setup Optimisation Results")
+            display_df = st.session_state.confidence_results_df.head(60)
+            st.dataframe(display_df.style.format({
+                "Avg Profit/Trade": "{:.2%}", "Good/Bad Ratio": "{:.2f}",
+                "Avg Entry Conf.": "{:.1f}%", "Good Score": "{:.4f}",
+                "Bad Score": "{:.4f}", "Norm. Score %": "{:.2f}%"
+            }))
+
+        elif st.session_state.get('single_ticker_results') is not None:
+            res = st.session_state.single_ticker_results
+            st.subheader(f"Results for {st.session_state.get('ticker_select')}")
+            c1, c2, c3, c4 = st.columns(4); c1.metric("Cumulative Long P&L", f"{res['long_pnl']:.2%}"); c2.metric("Avg Long Trade P&L", f"{res['avg_long_trade']:.2%}"); c3.metric("Cumulative Short P&L", f"{res['short_pnl']:.2%}"); c4.metric("Avg Short Trade P&L", f"{res['avg_short_trade']:.2%}")
+            if res['results_df'] is not None:
+                st.plotly_chart(generate_long_plot(res['results_df'], res['trades'], st.session_state.get('ticker_select')), use_container_width=True)
+                st.plotly_chart(generate_short_plot(res['results_df'], res['trades'], st.session_state.get('ticker_select')), use_container_width=True)
+
+        elif st.session_state.get('summary_df') is not None and not st.session_state.summary_df.empty:
+            display_summary_analytics(st.session_state.summary_df)
+            st.subheader("Results per Ticker")
+            if st.checkbox("Only show tickers with trades", value=True):
+                display_df = st.session_state.summary_df[(st.session_state.summary_df['Num Long Trades'] > 0) | (st.session_state.summary_df['Num Short Trades'] > 0)]
+            else:
+                display_df = st.session_state.summary_df
+            st.dataframe(display_df.style.format({"Cumulative Long P&L": "{:.2%}", "Avg Long Profit per Trade": "{:.2%}", "Cumulative Short P&L": "{:.2%}", "Avg Short Profit per Trade": "{:.2%}", "Avg Long Confidence": "{:.0f}%", "Avg Short Confidence": "{:.0f}%"}))
+
+        if st.session_state.get('open_trades_df') is not None and not st.session_state.open_trades_df.empty:
+            st.subheader("👨‍💼 Advisor: Currently Open Positions (Manual Run)")
+            display_open_df = st.session_state.open_trades_df.copy()
+            st.dataframe(display_open_df.style.format({"Date Open": "{:%Y-%m-%d}", "Start Confidence": "{:.0f}%", "Current % P/L": "{:.2%}"}))
+            
+            st.markdown("---")
+            st.info("Want to see open trades from a wider range of top strategies?")
+            if st.button("Run Advanced Advisor Report"):
+                st.session_state.run_advanced_advisor = True
+                st.rerun()
+        
+        def apply_best_params_to_widgets():
+            bp = st.session_state.get('best_params');
+            if not bp: return
+            st.session_state.ma_period, st.session_state.bb_period, st.session_state.bb_std = bp.get("large_ma_period"), bp.get("bband_period"), bp.get("bband_std_dev")
+            st.session_state.long_sl, st.session_state.short_sl = bp.get("long_stop_loss_pct") * 100, bp.get("short_stop_loss_pct") * 100
+            st.session_state.long_delay, st.session_state.short_delay = bp.get("long_delay_days"), bp.get("short_delay_days")
+            st.session_state.long_entry, st.session_state.short_entry = bp.get("long_entry_threshold_pct") * 100, bp.get("short_entry_threshold_pct") * 100
+            st.session_state.long_exit, st.session_state.short_exit = bp.get("long_exit_ma_threshold_pct") * 100, bp.get("short_exit_ma_threshold_pct") * 100
+            st.session_state.confidence_slider = bp.get("confidence_threshold")
+            st.session_state.best_params = None
+        
+        if st.session_state.get('best_params'):
+            st.button("⬇️ Load Optimal Parameters into Manual Settings", on_click=apply_best_params_to_widgets)
+        
+        if st.session_state.get('worst_confidence_setup'):
+            if st.button("Apply Worst Setup as Veto Filter"):
+                st.session_state.veto_setup = st.session_state.worst_confidence_setup
+                st.session_state.worst_confidence_setup = None
+                st.rerun()
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+# requirements.txt
+streamlit==1.33.0
+pandas==2.2.2
+plotly==5.22.0
+numpy==1.26.4
+pandas_ta