{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.graph_objs as go\n",
"from plotly.subplots import make_subplots\n",
"import logging\n",
"import numpy as np\n",
"import talib\n",
"\n",
"logging.basicConfig(level=logging.INFO)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and preprocess BTC/USDT data\n",
"Using kline data downloaded from binance with 5, 15 and 30 min interval."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def fill_missing_values(data, expected_interval):\n",
"\n",
" LOW_TIME = pd.Timestamp(year=2019, month=7, day=21, hour=0, minute=0)\n",
"\n",
" data = data.loc[data['close_time'] >= LOW_TIME]\n",
"\n",
" \n",
" num_new_intervals, num_fixed_intervals = 0, 0\n",
" expected_internal_interval = expected_interval - pd.Timedelta(milliseconds=1)\n",
" prev_row = data.iloc[0]\n",
" preprocessed_data = [prev_row]\n",
" for _, row in data.iloc[1:].iterrows():\n",
" interval = row['open_time'] - prev_row['open_time']\n",
" internal_interval = row['close_time'] - row['open_time']\n",
"\n",
" if internal_interval < expected_internal_interval:\n",
" row['close_time'] = row['open_time'] + expected_internal_interval\n",
" num_fixed_intervals += 1\n",
"\n",
" while interval != expected_interval:\n",
" prev_row = prev_row.copy()\n",
" prev_row['open_time'] = prev_row['open_time'] + expected_interval\n",
" prev_row['close_time'] = prev_row['close_time'] + expected_interval\n",
"\n",
" preprocessed_data.append(prev_row)\n",
" num_new_intervals += 1\n",
" interval = row['open_time'] - prev_row['open_time']\n",
"\n",
" prev_row = row\n",
" preprocessed_data.append(row)\n",
"\n",
" logging.info(\"Inserted %d new intervals.\", num_new_intervals)\n",
" logging.info(\"Fixed %d intervals.\", num_fixed_intervals)\n",
"\n",
" result = pd.DataFrame(preprocessed_data)\n",
" result = result.reset_index(drop=True)\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_btc_usdt_dataset(path, interval):\n",
" logging.info(\"=\"*80)\n",
" logging.info(\"Loading dataset '%s' with interval %s\", path, interval)\n",
" btc_usdt_data = pd.read_csv(path,\n",
" usecols=['Open time', 'Open price', 'High price', 'Low price', 'Close price', 'Volume', 'Close time'])\n",
" btc_usdt_data.rename(columns=lambda x: x.lower().replace(\" \", \"_\"), inplace=True)\n",
" btc_usdt_data['open_time'] = pd.to_datetime(btc_usdt_data['open_time'], unit='ms')\n",
" btc_usdt_data['close_time'] = pd.to_datetime(btc_usdt_data['close_time'], unit='ms')\n",
" btc_usdt_data = fill_missing_values(btc_usdt_data, interval)\n",
"\n",
" # Sanity check assertions\n",
" unique_intervals_open_time = (btc_usdt_data.shift(-1).dropna()['open_time'] - btc_usdt_data.iloc[:-1]['open_time']).unique()\n",
" unique_intervals_close_time = (btc_usdt_data.shift(-1).dropna()['close_time'] - btc_usdt_data.iloc[:-1]['close_time']).unique()\n",
" assert len(unique_intervals_open_time) == 1\n",
" assert len(unique_intervals_close_time) == 1\n",
" logging.info(\"There is the same interval between all open times: %s\", unique_intervals_open_time[0])\n",
" logging.info(\"There is the same interval between all close times: %s\", unique_intervals_close_time[0])\n",
"\n",
" # Print statistics\n",
" logging.info(\"First data point: %s\", btc_usdt_data['close_time'].iloc[0])\n",
" logging.info(\"Last data point: %s\", btc_usdt_data['close_time'].iloc[-1])\n",
" logging.info(\"Number of data points: %d\", len(btc_usdt_data))\n",
"\n",
" # Plot data\n",
" go.Figure(go.Scatter(y=btc_usdt_data['close_price'].iloc[::10], x=btc_usdt_data['close_time'].iloc[::10])).show()\n",
"\n",
" return btc_usdt_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"INTERVAL_5_MIN = pd.Timedelta(minutes=5)\n",
"INTERVAL_15_MIN = pd.Timedelta(minutes=15)\n",
"INTERVAL_30_MIN = pd.Timedelta(minutes=30)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:================================================================================\n",
"INFO:root:Loading dataset '../data/raw_data/btc-usdt-5m.csv' with interval 0 days 00:05:00\n",
"INFO:root:Inserted 614 new intervals.\n",
"INFO:root:Fixed 8 intervals.\n",
"INFO:root:There is the same interval between all open times: 0 days 00:05:00\n",
"INFO:root:There is the same interval between all close times: 0 days 00:05:00\n",
"INFO:root:First data point: 2019-07-21 00:04:59.999000\n",
"INFO:root:Last data point: 2024-07-27 09:44:59.999000\n",
"INFO:root:Number of data points: 528021\n"
]
},
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"plotlyServerURL": "https://plot.ly"
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" 11.74 | \n",
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" 11.74 | \n",
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" 30 | \n",
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" 11.74 | \n",
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" 2.189061 | \n",
" 2017-08-17 04:14:59.999 | \n",
" 11.74 | \n",
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" 30 | \n",
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\n",
" \n",
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" 4261.48 | \n",
" 4270.41 | \n",
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" 4261.45 | \n",
" 9.119865 | \n",
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" 11.74 | \n",
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" 4310.07 | \n",
" 21.923552 | \n",
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" 11.74 | \n",
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" 30 | \n",
"
\n",
" \n",
" | 3 | \n",
" 2017-08-17 04:45:00 | \n",
" 4310.07 | \n",
" 4313.62 | \n",
" 4291.37 | \n",
" 4308.83 | \n",
" 13.948531 | \n",
" 2017-08-17 04:59:59.999 | \n",
" 11.74 | \n",
" 1.16 | \n",
" 30 | \n",
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\n",
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" | 4 | \n",
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" 4308.83 | \n",
" 4328.69 | \n",
" 4304.31 | \n",
" 4304.31 | \n",
" 5.101153 | \n",
" 2017-08-17 05:14:59.999 | \n",
" 11.74 | \n",
" 1.16 | \n",
" 30 | \n",
"
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" 4261.32 | \n",
" 4261.45 | \n",
" 11.308926 | \n",
" 2017-08-17 04:29:59.999 | \n",
" 11.74 | \n",
" 1.16 | \n",
" 30 | \n",
"
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" 2017-08-17 04:59:59.999 | \n",
" 11.74 | \n",
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" 30 | \n",
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" 4308.83 | \n",
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" 21.048648 | \n",
" 2017-08-17 05:29:59.999 | \n",
" 11.74 | \n",
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" 11.74 | \n",
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" 30 | \n",
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" 4311.02 | \n",
" 3.566277 | \n",
" 2017-08-17 06:29:59.999 | \n",
" 11.74 | \n",
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"The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Successfully finished last run (ID:uzq8eg4p). Initializing new run:
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"wandb version 0.18.0 is available! To upgrade, please run:\n",
" $ pip install wandb --upgrade"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.17.7"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"Run data is saved locally in /Users/filipstefaniuk/Documents/wne-msc-thesis/notebooks/wandb/run-20240914_124941-bjkta134"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"Syncing run treasured-deluge-92 to Weights & Biases (docs)
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View project at https://wandb.ai/filipstefaniuk/wne-masters-thesis-testing"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" View run at https://wandb.ai/filipstefaniuk/wne-masters-thesis-testing/runs/bjkta134"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/var/folders/pz/gkm59rg174z0867wc4h3wd000000gn/T/tmpiyipoyei)... Done. 0.3s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"wandb: Network error (ConnectionError), entering retry loop.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:04:55.083906, resuming normal operation.\n"
]
}
],
"source": [
"WANDB_PROJECT = 'wne-masters-thesis-testing'\n",
"\n",
"upload_dataset_to_wandb(windows_5m_data, 'btc-usdt-5m', project=WANDB_PROJECT)\n",
"upload_dataset_to_wandb(windows_15m_data, 'btc-usdt-15m', project=WANDB_PROJECT)\n",
"upload_dataset_to_wandb(windows_30m_data, 'btc-usdt-30m', project=WANDB_PROJECT)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test if time series dataset can be correctly constructed"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:Validating if time series dataset can be constructed from 5m data.\n",
"INFO:root:Validating if time series dataset can be constructed from 15m data.\n",
"INFO:root:Validating if time series dataset can be constructed from 30m data.\n"
]
}
],
"source": [
"from ml.data import build_time_series_dataset\n",
"config = {\n",
" 'data': {\n",
" 'fields': {\n",
" 'time_index': 'time_index',\n",
" 'target': 'close_price',\n",
" 'group_ids': ['group_id'],\n",
" 'static_real': [],\n",
" 'static_cat': [],\n",
" 'dynamic_cat': [],\n",
" 'dynamic_known_real': [],\n",
" 'dynamic_known_cat': [],\n",
" 'dynamic_unknown_real': ['returns'],\n",
" 'dynamic_unknown_cat': []\n",
" }\n",
" },\n",
" 'past_window': 2,\n",
" 'future_window': 2,\n",
" }\n",
"\n",
"logging.info(\"Validating if time series dataset can be constructed from 5m data.\")\n",
"for in_sample, out_of_sample in windows_5m_data:\n",
" build_time_series_dataset(config, in_sample)\n",
" build_time_series_dataset(config, out_of_sample)\n",
"\n",
"logging.info(\"Validating if time series dataset can be constructed from 15m data.\")\n",
"for in_sample, out_of_sample in windows_15m_data:\n",
" build_time_series_dataset(config, in_sample)\n",
" build_time_series_dataset(config, out_of_sample)\n",
"\n",
"logging.info(\"Validating if time series dataset can be constructed from 30m data.\")\n",
"for in_sample, out_of_sample in windows_30m_data:\n",
" build_time_series_dataset(config, in_sample)\n",
" build_time_series_dataset(config, out_of_sample)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timedelta('1831 days 23:59:00')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Timestamp(year=2024, month=7, day=25, hour=23, minute=59) - pd.Timestamp(year=2019, month=7, day=21, hour=0, minute=0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.019178082191781"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1832 / 365"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.002292576419213974"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"4.2 / 1832"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "wnemsc",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 2
}