diff --git a/src/candidate_tests.ipynb b/src/candidate_tests.ipynb
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+++ b/src/candidate_tests.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A little non-economic research\n",
+ "\n",
+ "\n",
+ "## Existing quiz tests\n",
+ "\n",
+ "https://www.linkedin.com/skill-assessments/hub/quizzes/ \n",
+ "\n",
+ "![](../docs/li.png)\n",
+ "\n",
+ "https://www.w3schools.com/quiztest/quiztest.asp?qtest=PANDAS\n",
+ "\n",
+ "\n",
+ "![](../docs/w3.png)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Code interview\n",
+ "\n",
+ "Datasets:\n",
+ "\n",
+ "1. Binance Open Data: [spot candles](https://github.com/binance/binance-public-data/#klines)\n",
+ "2. OpenFIGI: [search API](https://www.openfigi.com/search).\n",
+ "\n",
+ "### Binance Open Data\n",
+ "\n",
+ "Downloading candles for `BTC/USDT` and `BTC/UDSC` using `bash` or `powershell`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "vscode": {
+ "languageId": "shellscript"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mkdir: cannot create directory ‘../data/binance’: File exists\n",
+ "--2022-06-23 12:48:13-- https://data.binance.vision/data/spot/daily/klines/BTCUSDT/1m/BTCUSDT-1m-2022-06-21.zip\n",
+ "Resolving data.binance.vision (data.binance.vision)... 13.224.2.90, 13.224.2.55, 13.224.2.128, ...\n",
+ "Connecting to data.binance.vision (data.binance.vision)|13.224.2.90|:443... connected.\n",
+ "HTTP request sent, awaiting response... 304 Not Modified\n",
+ "File ‘../data/binance/BTCUSDT-1m-2022-06-21.zip’ not modified on server. Omitting download.\n",
+ "\n",
+ "--2022-06-23 12:48:14-- https://data.binance.vision/data/spot/daily/klines/BTCUSDC/1m/BTCUSDC-1m-2022-06-21.zip\n",
+ "Resolving data.binance.vision (data.binance.vision)... 13.224.2.90, 13.224.2.55, 13.224.2.128, ...\n",
+ "Connecting to data.binance.vision (data.binance.vision)|13.224.2.90|:443... connected.\n",
+ "HTTP request sent, awaiting response... 304 Not Modified\n",
+ "File ‘../data/binance/BTCUSDC-1m-2022-06-21.zip’ not modified on server. Omitting download.\n",
+ "\n",
+ "Archive: ../data/binance/BTCUSDT-1m-2022-06-21.zip\n",
+ " inflating: ../data/binance/BTCUSDT-1m-2022-06-21.csv \n",
+ "Archive: ../data/binance/BTCUSDC-1m-2022-06-21.zip\n",
+ " inflating: ../data/binance/BTCUSDC-1m-2022-06-21.csv \n"
+ ]
+ }
+ ],
+ "source": [
+ "#!/bin/sh\n",
+ "\n",
+ "# create dir for data\n",
+ "!mkdir ../data/binance\n",
+ "\n",
+ "# download data using GET request\n",
+ "!wget -N -P ../data/binance https://data.binance.vision/data/spot/daily/klines/BTCUSDT/1m/BTCUSDT-1m-2022-06-21.zip\n",
+ "!wget -N -P../data/binance https://data.binance.vision/data/spot/daily/klines/BTCUSDC/1m/BTCUSDC-1m-2022-06-21.zip\n",
+ "\n",
+ "# unzip\n",
+ "!unzip -o -d ../data/binance ../data/binance/BTCUSDT-1m-2022-06-21.zip \n",
+ "!unzip -o -d ../data/binance ../data/binance/BTCUSDC-1m-2022-06-21.zip"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import packages for data analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "import httpx\n",
+ "\n",
+ "from datetime import datetime"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Read data from CSV file to Pandas DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
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+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "def get_data(pair: str) -> pd.DataFrame:\n",
+ " return pd.read_csv(f'../data/binance/{pair}-1m-2022-06-21.csv', header = None)\n",
+ "\n",
+ "btcusdt_df = get_data('BTCUSDT')\n",
+ "btcusdt_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set names to columns with 1m candles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " Low | \n",
+ " Close | \n",
+ " Volume | \n",
+ " Close_time | \n",
+ " Quote_asset_volume | \n",
+ " Number_of_trades | \n",
+ " Taker_buy_base_asset_volume | \n",
+ " Taker_buy_quote_asset_volume | \n",
+ " Ignore | \n",
+ "
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+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def set_column_names(df: pd.DataFrame) -> pd.DataFrame:\n",
+ " column_names_mapping = {\n",
+ " 0: 'Open_time',\n",
+ " 1: 'Open',\n",
+ " 2: 'High',\n",
+ " 3: 'Low',\n",
+ " 4: 'Close',\n",
+ " 5: 'Volume',\n",
+ " 6: 'Close_time',\n",
+ " 7: 'Quote_asset_volume',\n",
+ " 8: 'Number_of_trades',\n",
+ " 9: 'Taker_buy_base_asset_volume',\n",
+ " 10: 'Taker_buy_quote_asset_volume',\n",
+ " 11: 'Ignore'\n",
+ " }\n",
+ " return df.rename(columns=column_names_mapping)\n",
+ "\n",
+ "btcusdt_df = set_column_names(btcusdt_df)\n",
+ "btcusdt_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Convert timestamp to human-readable date and time format:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "btcusdt_df['Open_time'] = btcusdt_df.iloc[:, 0].apply(lambda t: datetime.fromtimestamp(t/1000))\n",
+ "btcusdt_df['Close_time'] = btcusdt_df.iloc[:, 6].apply(lambda t: datetime.fromtimestamp(t/1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Find min and max time:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " 2022-06-21 00:00:00 | \n",
+ " 2022-06-21 00:00:59.999000 | \n",
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+ " max | \n",
+ " 2022-06-21 23:59:00 | \n",
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+ "len 1440 1440"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "btcusdt_df[['Open_time', 'Close_time']].aggregate(func=[min, max, len])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Calculate 1-hour `OHLCV` candles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " 21457.82 | \n",
+ " 21195.70 | \n",
+ " 21242.94 | \n",
+ " 3755.82919 | \n",
+ " 2022-06-21 17:59:59.999 | \n",
+ "
\n",
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+ " 18 | \n",
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+ "
\n",
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\n",
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+ "
\n",
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\n",
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+ "
\n",
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+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Open High Low Close Volume Close_time\n",
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+ "19 21100.00 21306.51 20870.01 20888.64 4015.33528 2022-06-21 19:59:59.999\n",
+ "20 20888.63 20987.38 20666.00 20859.86 4442.87596 2022-06-21 20:59:59.999\n",
+ "21 20859.86 21054.99 20808.00 20972.91 1813.56236 2022-06-21 21:59:59.999\n",
+ "22 20972.91 21003.70 20741.03 20897.00 2945.61650 2022-06-21 22:59:59.999\n",
+ "23 20897.00 20943.17 20551.00 20723.52 2613.77441 2022-06-21 23:59:59.999"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def calculate_ohclv(df: pd.DataFrame) -> pd.DataFrame:\n",
+ " df['hour'] = df['Close_time'].apply(lambda t: t.hour)\n",
+ "\n",
+ " return (\n",
+ " df\n",
+ " .groupby(['hour'])\n",
+ " .agg(\n",
+ " {\n",
+ " 'Open': 'first',\n",
+ " 'High': max,\n",
+ " 'Low': min,\n",
+ " 'Close': 'last',\n",
+ " 'Volume': sum,\n",
+ " 'Close_time': max\n",
+ " }\n",
+ " )\n",
+ " .reset_index()\n",
+ " .drop(columns=['hour'])\n",
+ " )\n",
+ "\n",
+ "btcusdt_1h_df = calculate_ohclv(btcusdt_df)\n",
+ "btcusdt_1h_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validate results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert(\n",
+ " isinstance(btcusdt_1h_df, pd.DataFrame)\n",
+ " and btcusdt_1h_df.shape == (24, 6)\n",
+ " and not btcusdt_1h_df.isnull().any().any()\n",
+ " and btcusdt_1h_df.iloc[:, 0:5].ge(0).all().all()\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do the same for `BTC/USDC` pair:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ ],
+ "text/plain": [
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+ "21 20838.51 21057.01 20780.29 20958.22 99.09836 2022-06-21 21:59:59.999\n",
+ "22 20950.07 20975.92 20719.02 20875.37 177.08203 2022-06-21 22:59:59.999\n",
+ "23 20880.71 20916.85 20527.90 20699.78 173.22797 2022-06-21 23:59:59.999"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "btcusdc_df = get_data('BTCUSDC')\n",
+ "btcusdc_df = set_column_names(btcusdc_df)\n",
+ "btcusdc_df['Close_time'] = btcusdc_df.iloc[:, 6].apply(lambda t: datetime.fromtimestamp(t/1000))\n",
+ "\n",
+ "btcusdc_1h_df = calculate_ohclv(btcusdc_df)\n",
+ "btcusdc_1h_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Join altogether:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "btcusdt_1h_df['pair'] = 'BTC-USDT'\n",
+ "btcusdc_1h_df['pair'] = 'BTC-USDC'\n",
+ "\n",
+ "candles_1h_df = pd.concat([btcusdt_1h_df, btcusdc_1h_df])\n",
+ "\n",
+ "assert(\n",
+ " isinstance(candles_1h_df, pd.DataFrame)\n",
+ " and candles_1h_df.shape == (48, 7)\n",
+ " and (candles_1h_df['pair'].unique() == ['BTC-USDT', 'BTC-USDC']).all()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot something interesting... :bulb:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Enrich dataset using Open FIGI API Interaction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "JSONDecodeError",
+ "evalue": "Expecting value: line 1 column 1 (char 0)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/home/dictator/apps/resistance/src/candidate.ipynb Cell 24'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m json_response \u001b[39m=\u001b[39m response\u001b[39m.\u001b[39mjson()\n\u001b[1;32m 7\u001b[0m \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39mDataFrame\u001b[39m.\u001b[39mfrom_dict(json_response[\u001b[39m'\u001b[39m\u001b[39mresult\u001b[39m\u001b[39m'\u001b[39m], orient\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcolumns\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m send_request(\u001b[39m'\u001b[39;49m\u001b[39mBTCUSDT\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
+ "\u001b[1;32m/home/dictator/apps/resistance/src/candidate.ipynb Cell 24'\u001b[0m in \u001b[0;36msend_request\u001b[0;34m(ticker)\u001b[0m\n\u001b[1;32m 2\u001b[0m api_url \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mhttps://www.openfigi.com/search/query?facetQuery=MARKET_SECTOR_DES:%22Curncy%22&num_rows=100&simpleSearchString=\u001b[39m\u001b[39m{\u001b[39;00mticker\u001b[39m}\u001b[39;00m\u001b[39m&start=0\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 4\u001b[0m response \u001b[39m=\u001b[39m httpx\u001b[39m.\u001b[39mget(api_url)\n\u001b[0;32m----> 5\u001b[0m json_response \u001b[39m=\u001b[39m response\u001b[39m.\u001b[39;49mjson()\n\u001b[1;32m 7\u001b[0m \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39mDataFrame\u001b[39m.\u001b[39mfrom_dict(json_response[\u001b[39m'\u001b[39m\u001b[39mresult\u001b[39m\u001b[39m'\u001b[39m], orient\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcolumns\u001b[39m\u001b[39m'\u001b[39m)\n",
+ "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/httpx/_models.py:1517\u001b[0m, in \u001b[0;36mResponse.json\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[39mif\u001b[39;00m encoding \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m jsonlib\u001b[39m.\u001b[39mloads(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcontent\u001b[39m.\u001b[39mdecode(encoding), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m-> 1517\u001b[0m \u001b[39mreturn\u001b[39;00m jsonlib\u001b[39m.\u001b[39;49mloads(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtext, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+ "File \u001b[0;32m~/miniconda3/lib/python3.9/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 341\u001b[0m s \u001b[39m=\u001b[39m s\u001b[39m.\u001b[39mdecode(detect_encoding(s), \u001b[39m'\u001b[39m\u001b[39msurrogatepass\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 343\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 344\u001b[0m parse_int \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m parse_float \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 345\u001b[0m parse_constant \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_pairs_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m \u001b[39mreturn\u001b[39;00m _default_decoder\u001b[39m.\u001b[39;49mdecode(s)\n\u001b[1;32m 347\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 348\u001b[0m \u001b[39mcls\u001b[39m \u001b[39m=\u001b[39m JSONDecoder\n",
+ "File \u001b[0;32m~/miniconda3/lib/python3.9/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecode\u001b[39m(\u001b[39mself\u001b[39m, s, _w\u001b[39m=\u001b[39mWHITESPACE\u001b[39m.\u001b[39mmatch):\n\u001b[1;32m 333\u001b[0m \u001b[39m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[39m containing a JSON document).\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \n\u001b[1;32m 336\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mraw_decode(s, idx\u001b[39m=\u001b[39;49m_w(s, \u001b[39m0\u001b[39;49m)\u001b[39m.\u001b[39;49mend())\n\u001b[1;32m 338\u001b[0m end \u001b[39m=\u001b[39m _w(s, end)\u001b[39m.\u001b[39mend()\n\u001b[1;32m 339\u001b[0m \u001b[39mif\u001b[39;00m end \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(s):\n",
+ "File \u001b[0;32m~/miniconda3/lib/python3.9/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 353\u001b[0m obj, end \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscan_once(s, idx)\n\u001b[1;32m 354\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m \u001b[39mraise\u001b[39;00m JSONDecodeError(\u001b[39m\"\u001b[39m\u001b[39mExpecting value\u001b[39m\u001b[39m\"\u001b[39m, s, err\u001b[39m.\u001b[39mvalue) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m 356\u001b[0m \u001b[39mreturn\u001b[39;00m obj, end\n",
+ "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
+ ]
+ }
+ ],
+ "source": [
+ "def send_request(ticker: str) -> pd.DataFrame:\n",
+ " api_url = f'https://www.openfigi.com/search/query?facetQuery=MARKET_SECTOR_DES:%22Curncy%22&num_rows=100&simpleSearchString={ticker}&start=0'\n",
+ "\n",
+ " response = httpx.get(api_url)\n",
+ " json_response = response.json()\n",
+ " \n",
+ " return pd.DataFrame.from_dict(json_response['result'], orient='columns')\n",
+ "\n",
+ "send_request('BTCUSDT')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('base')",
+ "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": "3.9.13"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "6fd7ff10be7e3a66c1b3745c4cbc00041a2589eb74ab4be46a3698a7b56001aa"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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