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trading_analysis/Silver/COT_data_nasdaq_Silver.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "68e3c2ce-ed33-4c9d-851c-86184d9d3414",
"metadata": {},
"source": [
"# Load NASDAQ API key (Data Link API) - Silver\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7629bec6-f9e7-4180-aa56-0c2e857d34cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"# Load the environment variables from .env file\n",
"load_dotenv(\"api_keys\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "aa5b3443-449e-4f02-b231-1ffdef4d11ac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded key\n"
]
}
],
"source": [
"nasdaq_api_key = os.getenv('NASDAQ_API_KEY')\n",
"if len(nasdaq_api_key) > 0:\n",
" print(\"loaded key\")"
]
},
{
"cell_type": "markdown",
"id": "5fdcd717-d4a3-4e7e-b8b5-a4369bd1bc83",
"metadata": {},
"source": [
"## Retrieve 2 year of COT data (% shifts producers long / short)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "fc5d6a91-c107-443e-8b6e-84e10d945ca2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Open Interest Producer/Merchant/Processor/User Longs \\\n",
"Date \n",
"2023-11-28 139144.0 3472.0 \n",
"2023-12-05 139753.0 4714.0 \n",
"2023-12-12 134281.0 4311.0 \n",
"2023-12-19 127549.0 3701.0 \n",
"2023-12-26 131408.0 4063.0 \n",
"\n",
" Producer/Merchant/Processor/User Shorts Swap Dealer Longs \\\n",
"Date \n",
"2023-11-28 40323.0 33045.0 \n",
"2023-12-05 41340.0 33328.0 \n",
"2023-12-12 38849.0 30707.0 \n",
"2023-12-19 39714.0 30908.0 \n",
"2023-12-26 41011.0 29911.0 \n",
"\n",
" Swap Dealer Shorts Swap Dealer Spreads Money Manager Longs \\\n",
"Date \n",
"2023-11-28 44049.0 3770.0 45604.0 \n",
"2023-12-05 48716.0 2932.0 43852.0 \n",
"2023-12-12 43665.0 2832.0 33472.0 \n",
"2023-12-19 43686.0 1911.0 34931.0 \n",
"2023-12-26 45585.0 2415.0 36546.0 \n",
"\n",
" Money Manager Shorts Money Manager Spreads \\\n",
"Date \n",
"2023-11-28 21842.0 6885.0 \n",
"2023-12-05 22322.0 7544.0 \n",
"2023-12-12 24805.0 8439.0 \n",
"2023-12-19 20060.0 6662.0 \n",
"2023-12-26 19749.0 6847.0 \n",
"\n",
" Other Reportable Longs Other Reportable Shorts \\\n",
"Date \n",
"2023-11-28 17276.0 6758.0 \n",
"2023-12-05 19389.0 4616.0 \n",
"2023-12-12 22212.0 2421.0 \n",
"2023-12-19 17700.0 2820.0 \n",
"2023-12-26 18013.0 2947.0 \n",
"\n",
" Other Reportable Spreads Total Reportable Longs \\\n",
"Date \n",
"2023-11-28 2445.0 112497.0 \n",
"2023-12-05 1607.0 113366.0 \n",
"2023-12-12 1706.0 103679.0 \n",
"2023-12-19 1577.0 97390.0 \n",
"2023-12-26 2137.0 99932.0 \n",
"\n",
" Total Reportable Shorts Non Reportable Longs \\\n",
"Date \n",
"2023-11-28 126072.0 26647.0 \n",
"2023-12-05 129077.0 26387.0 \n",
"2023-12-12 122717.0 30602.0 \n",
"2023-12-19 116430.0 30159.0 \n",
"2023-12-26 120691.0 31476.0 \n",
"\n",
" Non Reportable Shorts \n",
"Date \n",
"2023-11-28 13072.0 \n",
"2023-12-05 10676.0 \n",
"2023-12-12 11564.0 \n",
"2023-12-19 11119.0 \n",
"2023-12-26 10717.0 \n"
]
}
],
"source": [
"import requests\n",
"import pandas as pd\n",
"from datetime import datetime\n",
"import urllib.parse\n",
"\n",
"# API endpoint components\n",
"base_url = \"https://data.nasdaq.com/api/v3/datasets/\"\n",
"dataset = \"CFTC/084691_F_ALL\"\n",
"data_format = \".json\"\n",
"\n",
"# Parameters\n",
"params = {\n",
" \"start_date\": \"2022-01-01\",\n",
" \"end_date\": \"2024-01-01\",\n",
" \"api_key\": nasdaq_api_key # Make sure this variable is defined\n",
"}\n",
"\n",
"# Construct the full URL with parameters\n",
"param_string = urllib.parse.urlencode(params)\n",
"url = f\"{base_url}{dataset}{data_format}?{param_string}\"\n",
"\n",
"# Make the API request\n",
"response = requests.get(url)\n",
"\n",
"# Check if the request was successful\n",
"if response.status_code == 200:\n",
" # Parse the JSON response\n",
" data = response.json()\n",
" \n",
" # Extract the dataset from the response\n",
" dataset = data['dataset']\n",
" \n",
" # Create a pandas DataFrame\n",
" df = pd.DataFrame(dataset['data'], columns=dataset['column_names'])\n",
" \n",
" # Convert the date column to datetime\n",
" df['Date'] = pd.to_datetime(df['Date'])\n",
" \n",
" # Set the date as the index\n",
" df.set_index('Date', inplace=True)\n",
" \n",
" # Sort the DataFrame by date\n",
" df.sort_index(inplace=True)\n",
" \n",
" # Display the first few rows of the DataFrame\n",
" print(df.tail())\n",
" \n",
" # You can now use this DataFrame for further analysis or visualization\n",
" \n",
"else:\n",
" print(f\"Error fetching data: {response.status_code}\")\n",
" print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a1ab3a82-5682-4701-ab9e-414d6d4ff115",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Open Interest', 'Producer/Merchant/Processor/User Longs',\n",
" 'Producer/Merchant/Processor/User Shorts', 'Swap Dealer Longs',\n",
" 'Swap Dealer Shorts', 'Swap Dealer Spreads', 'Money Manager Longs',\n",
" 'Money Manager Shorts', 'Money Manager Spreads',\n",
" 'Other Reportable Longs', 'Other Reportable Shorts',\n",
" 'Other Reportable Spreads', 'Total Reportable Longs',\n",
" 'Total Reportable Shorts', 'Non Reportable Longs',\n",
" 'Non Reportable Shorts'],\n",
" dtype='object')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "61ac72a5-b8f9-451f-ae0e-2c9b5ed7358a",
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>Open Interest</th>\n",
" <th>Producer/Merchant/Processor/User Longs</th>\n",
" <th>Producer/Merchant/Processor/User Shorts</th>\n",
" <th>Swap Dealer Longs</th>\n",
" <th>Swap Dealer Shorts</th>\n",
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" <th>Money Manager Shorts</th>\n",
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" <th>Other Reportable Shorts</th>\n",
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" <th>2023-11-28</th>\n",
" <td>139144.0</td>\n",
" <td>3472.0</td>\n",
" <td>40323.0</td>\n",
" <td>33045.0</td>\n",
" <td>44049.0</td>\n",
" <td>3770.0</td>\n",
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" <th>2023-12-05</th>\n",
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" <td>41340.0</td>\n",
" <td>33328.0</td>\n",
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" <td>4311.0</td>\n",
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" <td>30602.0</td>\n",
" <td>11564.0</td>\n",
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" <tr>\n",
" <th>2023-12-19</th>\n",
" <td>127549.0</td>\n",
" <td>3701.0</td>\n",
" <td>39714.0</td>\n",
" <td>30908.0</td>\n",
" <td>43686.0</td>\n",
" <td>1911.0</td>\n",
" <td>34931.0</td>\n",
" <td>20060.0</td>\n",
" <td>6662.0</td>\n",
" <td>17700.0</td>\n",
" <td>2820.0</td>\n",
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" <th>2023-12-26</th>\n",
" <td>131408.0</td>\n",
" <td>4063.0</td>\n",
" <td>41011.0</td>\n",
" <td>29911.0</td>\n",
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"text/plain": [
" Open Interest Producer/Merchant/Processor/User Longs \\\n",
"Date \n",
"2023-11-28 139144.0 3472.0 \n",
"2023-12-05 139753.0 4714.0 \n",
"2023-12-12 134281.0 4311.0 \n",
"2023-12-19 127549.0 3701.0 \n",
"2023-12-26 131408.0 4063.0 \n",
"\n",
" Producer/Merchant/Processor/User Shorts Swap Dealer Longs \\\n",
"Date \n",
"2023-11-28 40323.0 33045.0 \n",
"2023-12-05 41340.0 33328.0 \n",
"2023-12-12 38849.0 30707.0 \n",
"2023-12-19 39714.0 30908.0 \n",
"2023-12-26 41011.0 29911.0 \n",
"\n",
" Swap Dealer Shorts Swap Dealer Spreads Money Manager Longs \\\n",
"Date \n",
"2023-11-28 44049.0 3770.0 45604.0 \n",
"2023-12-05 48716.0 2932.0 43852.0 \n",
"2023-12-12 43665.0 2832.0 33472.0 \n",
"2023-12-19 43686.0 1911.0 34931.0 \n",
"2023-12-26 45585.0 2415.0 36546.0 \n",
"\n",
" Money Manager Shorts Money Manager Spreads \\\n",
"Date \n",
"2023-11-28 21842.0 6885.0 \n",
"2023-12-05 22322.0 7544.0 \n",
"2023-12-12 24805.0 8439.0 \n",
"2023-12-19 20060.0 6662.0 \n",
"2023-12-26 19749.0 6847.0 \n",
"\n",
" Other Reportable Longs Other Reportable Shorts \\\n",
"Date \n",
"2023-11-28 17276.0 6758.0 \n",
"2023-12-05 19389.0 4616.0 \n",
"2023-12-12 22212.0 2421.0 \n",
"2023-12-19 17700.0 2820.0 \n",
"2023-12-26 18013.0 2947.0 \n",
"\n",
" Other Reportable Spreads Total Reportable Longs \\\n",
"Date \n",
"2023-11-28 2445.0 112497.0 \n",
"2023-12-05 1607.0 113366.0 \n",
"2023-12-12 1706.0 103679.0 \n",
"2023-12-19 1577.0 97390.0 \n",
"2023-12-26 2137.0 99932.0 \n",
"\n",
" Total Reportable Shorts Non Reportable Longs \\\n",
"Date \n",
"2023-11-28 126072.0 26647.0 \n",
"2023-12-05 129077.0 26387.0 \n",
"2023-12-12 122717.0 30602.0 \n",
"2023-12-19 116430.0 30159.0 \n",
"2023-12-26 120691.0 31476.0 \n",
"\n",
" Non Reportable Shorts \n",
"Date \n",
"2023-11-28 13072.0 \n",
"2023-12-05 10676.0 \n",
"2023-12-12 11564.0 \n",
"2023-12-19 11119.0 \n",
"2023-12-26 10717.0 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "markdown",
"id": "96e0f458-9969-41a5-a153-9229c441eee5",
"metadata": {},
"source": [
"## Plot the data"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "0babd084-6190-4681-a6a3-23a95b92a68c",
"metadata": {},
"outputs": [
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"\n",
"Metrics for Producer/Merchant/Processor/User Longs:\n",
"Volatility (Standard Deviation): 1.36%\n",
"Average: 3.23%\n",
"Median: 3.00%\n",
"Minimum: 0.64%\n",
"Maximum: 7.14%\n",
"Typical Range: 2.37% to 3.78%\n",
"\n",
"Metrics for Producer/Merchant/Processor/User Shorts:\n",
"Volatility (Standard Deviation): 3.96%\n",
"Average: 25.57%\n",
"Median: 25.32%\n",
"Minimum: 18.08%\n",
"Maximum: 32.77%\n",
"Typical Range: 22.26% to 28.93%\n",
"\n",
"Correlation between Longs and Shorts: -0.22\n"
]
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],
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"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"\n",
"# Assuming df is your DataFrame with the COT data\n",
"# If not, you'll need to load your data here\n",
"\n",
"# Calculate percentages for producers\n",
"categories = [\n",
" 'Producer/Merchant/Processor/User Longs',\n",
" 'Producer/Merchant/Processor/User Shorts'\n",
"]\n",
"\n",
"for category in categories:\n",
" df[f'{category} %'] = df[category] / df['Open Interest'] * 100\n",
"\n",
"# Create the main figure\n",
"fig = go.Figure()\n",
"\n",
"# Add traces for each category\n",
"colors = ['blue', 'red']\n",
"for i, category in enumerate(categories):\n",
" fig.add_trace(go.Scatter(\n",
" x=df.index, \n",
" y=df[f'{category} %'], \n",
" mode='lines',\n",
" name=category,\n",
" line=dict(width=2, color=colors[i])\n",
" ))\n",
"\n",
"# Update layout\n",
"fig.update_layout(\n",
" title='COT Data: Percentage of Open Interest for Producers',\n",
" yaxis_title='Percentage of Open Interest',\n",
" xaxis_title='Date',\n",
" legend_title='Producer Categories',\n",
" height=600,\n",
" width=1200,\n",
" hovermode='x unified'\n",
")\n",
"\n",
"# Improve date labeling for better readability\n",
"fig.update_xaxes(\n",
" tickformat=\"%d %b %Y\",\n",
" tickangle=45,\n",
" dtick=\"M1\",\n",
" ticklabelmode=\"period\"\n",
")\n",
"\n",
"# Set y-axis to percentage\n",
"fig.update_yaxes(ticksuffix='%')\n",
"\n",
"# Show the plot\n",
"fig.show()\n",
"\n",
"# Calculate and print metrics\n",
"for category in categories:\n",
" print(f\"\\nMetrics for {category}:\")\n",
" data = df[f'{category} %']\n",
" \n",
" print(f\"Volatility (Standard Deviation): {data.std():.2f}%\")\n",
" print(f\"Average: {data.mean():.2f}%\")\n",
" print(f\"Median: {data.median():.2f}%\")\n",
" print(f\"Minimum: {data.min():.2f}%\")\n",
" print(f\"Maximum: {data.max():.2f}%\")\n",
" \n",
" # Calculate typical range\n",
" typical_low = np.percentile(data, 25)\n",
" typical_high = np.percentile(data, 75)\n",
" print(f\"Typical Range: {typical_low:.2f}% to {typical_high:.2f}%\")\n",
"\n",
"# Calculate and print the correlation between longs and shorts\n",
"correlation = df[f'{categories[0]} %'].corr(df[f'{categories[1]} %'])\n",
"print(f\"\\nCorrelation between Longs and Shorts: {correlation:.2f}\")"
]
},
{
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"id": "a7f6c187-39dc-4950-9e3f-0a328825c443",
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"text": [
"\n",
"Metrics for Open Interest:\n",
"Volatility (Standard Deviation): 5894.28\n",
"Average: 131794.00\n",
"Median: 131408.00\n",
"Minimum: 123640.00\n",
"Maximum: 144138.00\n",
"Typical Range: 126377.50 to 135836.50\n",
"\n",
"Overall trend: Downward\n",
"\n",
"Significant points:\n",
"Peak on 2023-10-10 00:00:00: 126971.00\n",
"Peak on 2023-12-05 00:00:00: 139753.00\n",
"Trough on 2023-09-12 00:00:00: 125292.00\n",
"Trough on 2023-10-17 00:00:00: 123640.00\n",
"Trough on 2023-12-19 00:00:00: 127549.00\n",
"\n",
"Sudden changes (>5% day-to-day):\n",
"2023-12-19 00:00:00: -5.01% change\n"
]
}
],
"source": [
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"from scipy import signal\n",
"\n",
"# Assuming df is your DataFrame with the COT data\n",
"# If not, you'll need to load your data here\n",
"\n",
"# Create the main figure\n",
"fig = go.Figure()\n",
"\n",
"# Add trace for Open Interest\n",
"fig.add_trace(go.Scatter(\n",
" x=df.index, \n",
" y=df['Open Interest'], \n",
" mode='lines',\n",
" name='Open Interest',\n",
" line=dict(width=2, color='blue')\n",
"))\n",
"\n",
"# Update layout\n",
"fig.update_layout(\n",
" title='COT Data: Open Interest Over Time (Silver)',\n",
" yaxis_title='Open Interest',\n",
" xaxis_title='Date',\n",
" height=600,\n",
" width=800,\n",
" hovermode='x unified'\n",
")\n",
"\n",
"# Improve date labeling for better readability\n",
"fig.update_xaxes(\n",
" tickformat=\"%d %b %Y\",\n",
" tickangle=45,\n",
" dtick=\"M1\",\n",
" ticklabelmode=\"period\"\n",
")\n",
"\n",
"# Show the plot\n",
"fig.show()\n",
"\n",
"# Calculate and print metrics\n",
"print(\"\\nMetrics for Open Interest:\")\n",
"data = df['Open Interest']\n",
"\n",
"print(f\"Volatility (Standard Deviation): {data.std():.2f}\")\n",
"print(f\"Average: {data.mean():.2f}\")\n",
"print(f\"Median: {data.median():.2f}\")\n",
"print(f\"Minimum: {data.min():.2f}\")\n",
"print(f\"Maximum: {data.max():.2f}\")\n",
"\n",
"# Calculate typical range\n",
"typical_low = np.percentile(data, 25)\n",
"typical_high = np.percentile(data, 75)\n",
"print(f\"Typical Range: {typical_low:.2f} to {typical_high:.2f}\")\n",
"\n",
"# Identify trends\n",
"def identify_trend(series):\n",
" # Calculate the overall trend\n",
" trend = np.polyfit(range(len(series)), series, 1)[0]\n",
" if trend > 0:\n",
" return \"Upward\"\n",
" elif trend < 0:\n",
" return \"Downward\"\n",
" else:\n",
" return \"Stable\"\n",
"\n",
"trend = identify_trend(data)\n",
"print(f\"\\nOverall trend: {trend}\")\n",
"\n",
"# Identify significant points\n",
"def find_peaks(series, prominence=1000):\n",
" peaks, _ = signal.find_peaks(series, prominence=prominence)\n",
" troughs, _ = signal.find_peaks(-series, prominence=prominence)\n",
" return peaks, troughs\n",
"\n",
"peaks, troughs = find_peaks(data)\n",
"\n",
"print(\"\\nSignificant points:\")\n",
"for peak in peaks:\n",
" print(f\"Peak on {df.index[peak]}: {data.iloc[peak]:.2f}\")\n",
"for trough in troughs:\n",
" print(f\"Trough on {df.index[trough]}: {data.iloc[trough]:.2f}\")\n",
"\n",
"# Identify sudden changes\n",
"pct_change = data.pct_change()\n",
"sudden_changes = pct_change[abs(pct_change) > 0.05] # 5% threshold\n",
"if not sudden_changes.empty:\n",
" print(\"\\nSudden changes (>5% day-to-day):\")\n",
" for date, change in sudden_changes.items():\n",
" print(f\"{date}: {change*100:.2f}% change\")\n",
"else:\n",
" print(\"\\nNo sudden changes above 5% threshold detected.\")"
]
},
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Reference in New Issue Copy Permalink