diff --git a/research/notebooks/single_pair_test.ipynb b/research/notebooks/single_pair_test.ipynb
index 525641a..3f02227 100644
--- a/research/notebooks/single_pair_test.ipynb
+++ b/research/notebooks/single_pair_test.ipynb
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
@@ -76,7 +76,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
@@ -117,7 +117,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
@@ -283,7 +283,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
@@ -344,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
@@ -389,7 +389,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
@@ -481,7 +481,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
@@ -552,7 +552,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
@@ -601,18 +601,21 @@
" symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n",
" symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n",
"\n",
+ " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
+ " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
+ "\n",
" print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n",
" print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n",
"\n",
" # Create subplots with price charts at bottom\n",
" fig = make_subplots(\n",
" rows=4, cols=1,\n",
- " row_heights=[0.33, 0.1, 0.33, 0.33],\n",
+ " row_heights=[0.25, 0.25, 0.25, 0.25],\n",
" subplot_titles=[\n",
" f'Testing Period: Scaled Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n",
- " f'Trading Signal Timeline ({TRD_DATE})',\n",
+ " f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n",
" f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',\n",
- " f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})'\n",
+ " f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',\n",
" ],\n",
" vertical_spacing=0.06,\n",
" specs=[[{\"secondary_y\": False}],\n",
@@ -689,43 +692,133 @@
" row=1, col=1\n",
" )\n",
"\n",
+ " # Add normalized price lines\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=price_data['tstamp'],\n",
+ " y=norm_a,\n",
+ " name=f'{SYMBOL_A} (Normalized)',\n",
+ " line=dict(color='blue', width=2),\n",
+ " opacity=0.8\n",
+ " ),\n",
+ " row=2, col=1\n",
+ " )\n",
+ "\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=price_data['tstamp'],\n",
+ " y=norm_b,\n",
+ " name=f'{SYMBOL_B} (Normalized)',\n",
+ " line=dict(color='orange', width=2),\n",
+ " opacity=0.8,\n",
+ " ),\n",
+ " row=2, col=1\n",
+ " )\n",
+ "\n",
+ " # Add BUY and SELL signals if available\n",
+ " if pair_trades is not None and len(pair_trades) > 0:\n",
+ " # Define signal groups to avoid legend repetition\n",
+ " signal_groups = {}\n",
+ " \n",
+ " # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n",
+ " for _, trade in pair_trades.iterrows():\n",
+ " symbol = trade['symbol']\n",
+ " side = trade['side']\n",
+ " status = trade['status']\n",
+ " \n",
+ " # Create signal group key (without status to combine OPEN/CLOSE)\n",
+ " signal_key = f\"{symbol} {side}\"\n",
+ " \n",
+ " # Find normalized price for this trade\n",
+ " trade_time = trade['time']\n",
+ " if symbol == SYMBOL_A:\n",
+ " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
+ " if closest_idx < len(norm_a):\n",
+ " norm_price = norm_a.iloc[closest_idx]\n",
+ " else:\n",
+ " norm_price = norm_a.iloc[-1]\n",
+ " else: # SYMBOL_B\n",
+ " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
+ " if closest_idx < len(norm_b):\n",
+ " norm_price = norm_b.iloc[closest_idx]\n",
+ " else:\n",
+ " norm_price = norm_b.iloc[-1]\n",
+ " \n",
+ " # Initialize group if not exists\n",
+ " if signal_key not in signal_groups:\n",
+ " signal_groups[signal_key] = {\n",
+ " 'times': [],\n",
+ " 'prices': [],\n",
+ " 'actual_prices': [],\n",
+ " 'symbol': symbol,\n",
+ " 'side': side,\n",
+ " 'status': status\n",
+ " }\n",
+ " \n",
+ " # Add to group\n",
+ " signal_groups[signal_key]['times'].append(trade_time)\n",
+ " signal_groups[signal_key]['prices'].append(norm_price)\n",
+ " signal_groups[signal_key]['actual_prices'].append(trade['price'])\n",
+ " \n",
+ " # Add each signal group as a single trace\n",
+ " for signal_key, group_data in signal_groups.items():\n",
+ " symbol = group_data['symbol']\n",
+ " side = group_data['side']\n",
+ " status = group_data['status']\n",
+ " \n",
+ " # Determine marker properties (same for all OPEN/CLOSE of same side)\n",
+ " if 'BUY' in side:\n",
+ " marker_color = 'green'\n",
+ " marker_symbol = 'triangle-up'\n",
+ " marker_size = 14\n",
+ " else: # SELL\n",
+ " marker_color = 'red'\n",
+ " marker_symbol = 'triangle-down'\n",
+ " marker_size = 14\n",
+ " \n",
+ " # Create hover text for each point in the group\n",
+ " hover_texts = []\n",
+ " for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n",
+ " group_data['prices'], \n",
+ " group_data['actual_prices'])):\n",
+ " # Find the corresponding trade to get the status for hover text\n",
+ " trade_info = pair_trades[(pair_trades['time'] == time) & \n",
+ " (pair_trades['symbol'] == symbol) & \n",
+ " (pair_trades['side'] == side)]\n",
+ " if len(trade_info) > 0:\n",
+ " trade_status = trade_info.iloc[0]['status']\n",
+ " hover_texts.append(f'{signal_key} {trade_status}
' +\n",
+ " f'Time: {time}
' +\n",
+ " f'Normalized Price: {norm_price:.4f}
' +\n",
+ " f'Actual Price: ${actual_price:.2f}')\n",
+ " else:\n",
+ " hover_texts.append(f'{signal_key}
' +\n",
+ " f'Time: {time}
' +\n",
+ " f'Normalized Price: {norm_price:.4f}
' +\n",
+ " f'Actual Price: ${actual_price:.2f}')\n",
+ " \n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=group_data['times'],\n",
+ " y=group_data['prices'],\n",
+ " mode='markers',\n",
+ " name=signal_key,\n",
+ " marker=dict(\n",
+ " color=marker_color,\n",
+ " size=marker_size,\n",
+ " symbol=marker_symbol,\n",
+ " # line=dict(width=2, color='black')\n",
+ " ),\n",
+ " showlegend=True,\n",
+ " hovertemplate='%{text}',\n",
+ " text=hover_texts\n",
+ " ),\n",
+ " row=2, col=1\n",
+ " )\n",
+ "\n",
" # ----------------------------- \n",
" # 2. Trading signals timeline if available - using consistent timeline\n",
- " if pair_trades is not None and len(pair_trades) > 0:\n",
" \n",
- " open_trades = pair_trades[(pair_trades['status'] == 'OPEN')]\n",
- " close_trades = pair_trades[(pair_trades['status'] == 'CLOSE')]\n",
- " # Create y-values for timeline visualization\n",
- " trade_indices = list(range(len(pair_trades)))\n",
- " \n",
- " zeroes = [0] * len(pair_trades)\n",
- " ones = [1] * len(pair_trades)\n",
- "\n",
- " # Add trading signals with different colors based on action and status\n",
- " if len(open_trades) > 0:\n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=open_trades['time'],\n",
- " y=zeroes,\n",
- " mode='markers',\n",
- " name='OPEN',\n",
- " marker=dict(color='green', size=10, symbol='triangle-up')\n",
- " ),\n",
- " row=2, col=1\n",
- " )\n",
- " \n",
- " if len(close_trades) > 0:\n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=close_trades['time'],\n",
- " y=ones,\n",
- " mode='markers',\n",
- " name='CLOSE',\n",
- " marker=dict(color='red', size=10, symbol='triangle-down')\n",
- " ),\n",
- " row=2, col=1\n",
- " )\n",
- " # ----------------------------- \n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=symbol_a_data['tstamp'],\n",
@@ -737,7 +830,6 @@
" row=3, col=1\n",
" )\n",
"\n",
- " if pair_trades is not None and len(pair_trades) > 0:\n",
" # Filter trades for Symbol_A\n",
" symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n",
" print(f\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n",
@@ -896,7 +988,7 @@
" \n",
" # Update layout\n",
" fig.update_layout(\n",
- " height=1200,\n",
+ " height=1600,\n",
" title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
" showlegend=True,\n",
" template=\"plotly_white\",\n",
@@ -905,9 +997,9 @@
" \n",
" # Update y-axis labels\n",
" fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n",
- " fig.update_yaxes(title_text=\"Open/Close Actions\", row=2, col=1)\n",
- " fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=3, col=1)\n",
- " fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=4, col=1)\n",
+ " fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=2, col=1)\n",
+ " fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=3, col=1)\n",
+ " fig.update_yaxes(title_text=\"Normalized Price (Base = 1.0)\", row=4, col=1)\n",
" \n",
" # Update x-axis labels and ensure consistent time range\n",
" time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n",
@@ -917,160 +1009,12 @@
" fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n",
" \n",
" # Display using plotly offline mode\n",
- " pyo.iplot(fig)\n",
- "\n",
+ " # pyo.iplot(fig)\n",
+ " fig.show()\n",
+ " \n",
" else:\n",
" print(\"No interactive visualization data available - strategy may not have run successfully\")\n",
"\n",
- "\n",
- "\n",
- " # Calculate normalized prices (base = 1.0)\n",
- " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
- " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
- "\n",
- " # Create the main figure\n",
- " fig = go.Figure()\n",
- "\n",
- " # Add normalized price lines\n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=price_data['tstamp'],\n",
- " y=norm_a,\n",
- " name=f'{SYMBOL_A} (Normalized)',\n",
- " line=dict(color='blue', width=2),\n",
- " opacity=0.8\n",
- " )\n",
- " )\n",
- "\n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=price_data['tstamp'],\n",
- " y=norm_b,\n",
- " name=f'{SYMBOL_B} (Normalized)',\n",
- " line=dict(color='orange', width=2),\n",
- " opacity=0.8,\n",
- " )\n",
- " )\n",
- "\n",
- " # Add BUY and SELL signals if available\n",
- " if pair_trades is not None and len(pair_trades) > 0:\n",
- " # Define signal groups to avoid legend repetition\n",
- " signal_groups = {}\n",
- " \n",
- " # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n",
- " for _, trade in pair_trades.iterrows():\n",
- " symbol = trade['symbol']\n",
- " side = trade['side']\n",
- " status = trade['status']\n",
- " \n",
- " # Create signal group key (without status to combine OPEN/CLOSE)\n",
- " signal_key = f\"{symbol} {side}\"\n",
- " \n",
- " # Find normalized price for this trade\n",
- " trade_time = trade['time']\n",
- " if symbol == SYMBOL_A:\n",
- " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
- " if closest_idx < len(norm_a):\n",
- " norm_price = norm_a.iloc[closest_idx]\n",
- " else:\n",
- " norm_price = norm_a.iloc[-1]\n",
- " else: # SYMBOL_B\n",
- " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
- " if closest_idx < len(norm_b):\n",
- " norm_price = norm_b.iloc[closest_idx]\n",
- " else:\n",
- " norm_price = norm_b.iloc[-1]\n",
- " \n",
- " # Initialize group if not exists\n",
- " if signal_key not in signal_groups:\n",
- " signal_groups[signal_key] = {\n",
- " 'times': [],\n",
- " 'prices': [],\n",
- " 'actual_prices': [],\n",
- " 'symbol': symbol,\n",
- " 'side': side,\n",
- " 'status': status\n",
- " }\n",
- " \n",
- " # Add to group\n",
- " signal_groups[signal_key]['times'].append(trade_time)\n",
- " signal_groups[signal_key]['prices'].append(norm_price)\n",
- " signal_groups[signal_key]['actual_prices'].append(trade['price'])\n",
- " \n",
- " # Add each signal group as a single trace\n",
- " for signal_key, group_data in signal_groups.items():\n",
- " symbol = group_data['symbol']\n",
- " side = group_data['side']\n",
- " status = group_data['status']\n",
- " \n",
- " # Determine marker properties (same for all OPEN/CLOSE of same side)\n",
- " if 'BUY' in side:\n",
- " marker_color = 'green' if symbol == SYMBOL_A else 'darkgreen'\n",
- " marker_symbol = 'triangle-up'\n",
- " marker_size = 14\n",
- " else: # SELL\n",
- " marker_color = 'red' if symbol == SYMBOL_A else 'darkred'\n",
- " marker_symbol = 'triangle-down'\n",
- " marker_size = 14\n",
- " \n",
- " # Create hover text for each point in the group\n",
- " hover_texts = []\n",
- " for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n",
- " group_data['prices'], \n",
- " group_data['actual_prices'])):\n",
- " # Find the corresponding trade to get the status for hover text\n",
- " trade_info = pair_trades[(pair_trades['time'] == time) & \n",
- " (pair_trades['symbol'] == symbol) & \n",
- " (pair_trades['side'] == side)]\n",
- " if len(trade_info) > 0:\n",
- " trade_status = trade_info.iloc[0]['status']\n",
- " hover_texts.append(f'{signal_key} {trade_status}
' +\n",
- " f'Time: {time}
' +\n",
- " f'Normalized Price: {norm_price:.4f}
' +\n",
- " f'Actual Price: ${actual_price:.2f}')\n",
- " else:\n",
- " hover_texts.append(f'{signal_key}
' +\n",
- " f'Time: {time}
' +\n",
- " f'Normalized Price: {norm_price:.4f}
' +\n",
- " f'Actual Price: ${actual_price:.2f}')\n",
- " \n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=group_data['times'],\n",
- " y=group_data['prices'],\n",
- " mode='markers',\n",
- " name=signal_key,\n",
- " marker=dict(\n",
- " color=marker_color,\n",
- " size=marker_size,\n",
- " symbol=marker_symbol,\n",
- " line=dict(width=2, color='black')\n",
- " ),\n",
- " showlegend=True,\n",
- " hovertemplate='%{text}',\n",
- " text=hover_texts\n",
- " )\n",
- " )\n",
- "\n",
- " # Update layout\n",
- " fig.update_layout(\n",
- " title=f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n",
- " xaxis_title='Time',\n",
- " yaxis_title='Normalized Price (Base = 1.0)',\n",
- " height=600,\n",
- " showlegend=True,\n",
- " template=\"plotly_white\",\n",
- " hovermode='x unified',\n",
- " plot_bgcolor='lightgray',\n",
- " )\n",
- "\n",
- " # Add horizontal line at y=1.0 for reference\n",
- " fig.add_hline(y=1.0, line_dash=\"dash\", line_color=\"gray\", opacity=0.5, \n",
- " annotation_text=\"Baseline (1.0)\")\n",
- "\n",
- " # Display the chart\n",
- " fig.show()\n",
- "\n",
" print(f\"\\nChart shows:\")\n",
" print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n",
" print(f\"- BUY signals shown as green triangles pointing up\")\n",
@@ -1101,7 +1045,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
@@ -1171,7 +1115,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
@@ -1252,7 +1196,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 108,
"metadata": {},
"outputs": [
{
@@ -1688,9 +1632,7 @@
"data": {
"application/vnd.plotly.v1+json": {
"config": {
- "linkText": "Export to plot.ly",
- "plotlyServerURL": "https://plot.ly",
- "showLink": false
+ "plotlyServerURL": "https://plot.ly"
},
"data": [
{
@@ -2460,156 +2402,1506 @@
"yaxis": "y"
},
{
+ "line": {
+ "color": "blue",
+ "width": 2
+ },
+ "name": "ADA-USDT (Normalized)",
+ "opacity": 0.8,
+ "type": "scatter",
+ "x": [
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