1258 lines
40 KiB
Plaintext
1258 lines
40 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from imbalance_price_calculator import ImbalancePriceCalculator\n",
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"\n",
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"# auto reload modules\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[16.0, 154.25, 154.25, 1.0], [40.0, 159.25, 159.25, 1.0], [20.0, 159.25, 159.25, 1.0], [52.1, 194.6, 279.69, 2.0], [58.0, 194.6, 279.84, 2.0], [39.0, 243.97, 592.87, 2.0], [15.0, 515.6, 636.81, 2.0], [1.3, 515.6, 638.3, 2.0], [0.8, 515.6, 642.18, 2.0], [15.0, 515.6, 643.81, 2.0], [1.0, 526.6, 651.6, 2.0], [150.0, 243.97, 701.68, 2.0], [19.0, 1000.0, 1000.0, 2.0], [1.0, 1500.0, 1500.0, 2.0], [2.0, 896.22, 1696.22, 2.0], [10.0, 1815.0, 1815.0, 2.0], [97.1, 2000.0, 2000.0, 2.0], [79.0, 2867.35, 2867.35, 2.0], [64.0, 202.2, 279.45, 2.0], [64.0, 202.3, 279.55, 2.0], [40.0, 205.3, 326.48, 2.0], [195.0, 145.8, 337.1, 2.0], [330.0, 243.97, 373.89, 2.0], [1.0, 450.0, 450.0, 2.0], [450.0, 198.8, 544.25, 2.0], [43.0, 243.97, 553.07, 2.0], [0.6, 243.97, 592.87, 2.0], [210.0, 243.97, 602.27, 2.0], [16.0, 514.92, 630.53, 2.0], [1.5, 515.6, 636.81, 2.0], [15.0, 515.6, 642.18, 2.0], [0.6, 515.6, 643.81, 2.0], [15.0, 526.6, 651.6, 2.0], [44.9, 1450.0, 1450.0, 2.0], [7.0, 599.0, 599.0, 1.0], [5.0, 150.0, 150.0, 2.0], [1.0, 158.79, 158.79, 1.0], [33.0, 390.0, 390.0, 1.0], [6.0, 194.6, 279.69, 2.0], [0.8, 205.3, 326.48, 2.0], [25.0, 299.0, 496.76, 2.0], [1.7, 243.97, 553.07, 2.0], [1.3, 514.92, 630.53, 2.0], [15.0, 515.6, 638.3, 2.0], [375.0, 243.97, 643.02, 2.0], [225.0, 190.2, 647.07, 2.0], [16.0, 1450.0, 1450.0, 2.0], [0.5, 896.22, 1696.22, 2.0], [2.0, 800.0, 800.0, 3.0]]\n"
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]
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}
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],
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"source": [
|
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"# load data\n",
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"import pandas\n",
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"\n",
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"df = pandas.read_csv('../../data/ods139.csv', delimiter=';', decimal=\".\", thousands=',')\n",
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"df['Datetime'] = pandas.to_datetime(df['Datetime'])\n",
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"\n",
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"\n",
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"# get rows for 2023-12-07 09:45\n",
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"df = df[df['Datetime'] == '2023-12-09 12:00:00']\n",
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"# get bid ladder\n",
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"bid_ladder = df[['Bid Volume', 'Bid Price', 'Bid+Start Price', 'Activation Order']].values.tolist()\n",
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"\n",
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"print(bid_ladder)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(17.0, 158.79, 158.79, 1.0)\n",
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"(57.0, 159.25, 159.25, 1.0)\n",
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"(77.0, 159.25, 159.25, 1.0)\n",
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"(110.0, 390.0, 390.0, 1.0)\n",
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"(117.0, 599.0, 599.0, 1.0)\n",
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"(312.0, 599.0, 145.8, 2.0)\n",
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"(317.0, 599.0, 150.0, 2.0)\n",
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"(542.0, 599.0, 190.2, 2.0)\n",
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"(594.1, 599.0, 194.6, 2.0)\n",
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"(652.1, 599.0, 194.6, 2.0)\n",
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"(1172.1, 599.0, 202.2, 2.0)\n",
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"(1236.1, 599.0, 202.3, 2.0)\n",
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"(1276.1, 599.0, 205.3, 2.0)\n",
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"(1276.8999999999999, 599.0, 205.3, 2.0)\n",
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"(1315.8999999999999, 599.0, 243.97, 2.0)\n",
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"(1465.8999999999999, 599.0, 243.97, 2.0)\n",
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"(1795.8999999999999, 599.0, 243.97, 2.0)\n",
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"(1838.8999999999999, 599.0, 243.97, 2.0)\n",
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"(1839.4999999999998, 599.0, 243.97, 2.0)\n",
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"(2049.5, 599.0, 243.97, 2.0)\n",
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"(2051.2, 599.0, 243.97, 2.0)\n",
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"(2426.2, 599.0, 243.97, 2.0)\n",
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"(2451.2, 599.0, 299.0, 2.0)\n",
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"(2452.2, 599.0, 450.0, 2.0)\n",
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"(2468.2, 599.0, 514.92, 2.0)\n",
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"(2469.5, 599.0, 514.92, 2.0)\n",
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"(2484.5, 599.0, 515.6, 2.0)\n",
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"(2485.8, 599.0, 515.6, 2.0)\n",
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"(2486.6000000000004, 599.0, 515.6, 2.0)\n",
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"(2501.6000000000004, 599.0, 515.6, 2.0)\n",
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"(2503.1000000000004, 599.0, 515.6, 2.0)\n",
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"(2518.1000000000004, 599.0, 515.6, 2.0)\n",
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"(2518.7000000000003, 599.0, 515.6, 2.0)\n",
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"(2533.7000000000003, 599.0, 515.6, 2.0)\n",
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"(2534.7000000000003, 599.0, 526.6, 2.0)\n",
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"(2549.7000000000003, 599.0, 526.6, 2.0)\n",
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"(2551.7000000000003, 896.22, 896.22, 2.0)\n",
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"(2552.2000000000003, 896.22, 896.22, 2.0)\n",
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"(2571.2000000000003, 1000.0, 1000.0, 2.0)\n",
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"(2616.1000000000004, 1450.0, 1450.0, 2.0)\n",
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"(2632.1000000000004, 1450.0, 1450.0, 2.0)\n",
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"(2633.1000000000004, 1500.0, 1500.0, 2.0)\n",
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"(2643.1000000000004, 1815.0, 1815.0, 2.0)\n",
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"(2740.2000000000003, 2000.0, 2000.0, 2.0)\n",
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"(2819.2000000000003, 2867.35, 2867.35, 2.0)\n",
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"(2821.2000000000003, 2867.35, 800.0, 3.0)\n"
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]
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}
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],
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"source": [
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"c = ImbalancePriceCalculator(bid_ladder)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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ladder\"},\"xaxis\":{\"title\":{\"text\":\"Volume\"}},\"yaxis\":{\"title\":{\"text\":\"Price\"}},\"hovermode\":\"x unified\"}, {\"responsive\": true} ).then(function(){\n",
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" \n",
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"var gd = document.getElementById('d393cf65-0497-4341-8bfc-54ee64ac2dfa');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"var outputEl = gd.closest('.output');\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" }) }; }); </script> </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"100MW: 390.0\n",
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"200MW: 599.0\n",
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"300MW: 599.0\n",
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"400MW: 599.0\n",
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"500MW: 599.0\n",
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"600MW: 599.0\n",
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"700MW: 599.0\n",
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"800MW: 599.0\n",
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"900MW: 599.0\n",
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"1000MW: 599.0\n",
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"Max: 2867.35\n"
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]
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}
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],
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"source": [
|
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"c.plot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"599.0\n"
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]
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}
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],
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"source": [
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"print(c.get_imbalance_price(1000))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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