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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from model_intra import get_data, walk_forward_validation\n",
"import lightgbm as lgb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"getting econ tickers: 100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n",
"Getting release dates: 100%|██████████| 8/8 [00:01<00:00, 4.87it/s]\n",
"Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3997.91it/s]\n",
"Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--spx_intra-e0e5e7af8fd43022/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
"Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 799.22it/s]\n"
]
}
],
"source": [
"data, df_final, final_row = get_data(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1\n",
"data['HighPct'] = (data['High'] / data['PrevClose']) - 1\n",
"data['LowPct'] = (data['Low'] / data['PrevClose']) - 1\n",
"data['ClosePct'] = data['ClosePct'].shift(-1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Calculate the rolling likelihood\n",
"rolling_likelihood = (data['H1Break'] & data['H1Touch']==True).rolling(window=100).mean()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"data['H1BreakTouchPct'] = data['H1Break'].expanding().sum() / data['H1Touch'].expanding().sum()\n",
"data['H2BreakTouchPct'] = data['H2Break'].expanding().sum() / data['H2Touch'].expanding().sum()\n",
"data['H1BreakTouchPct'] = data['L1Break'].expanding().sum() / data['L1Touch'].expanding().sum()\n",
"data['H2BreakTouchPct'] = data['L2Break'].expanding().sum() / data['L2Touch'].expanding().sum()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>H2Touch</th>\n",
" <th>H2Break</th>\n",
" <th>H2BreakTouch</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-07-02</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-03</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-05</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-06</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-09</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-10</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0.588235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-11</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.588235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-12</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.588235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-13</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.588235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-16</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0.571429</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1332 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" H2Touch H2Break H2BreakTouch\n",
"2018-07-02 False False NaN\n",
"2018-07-03 False False NaN\n",
"2018-07-05 False False NaN\n",
"2018-07-06 False False NaN\n",
"2018-07-09 False False NaN\n",
"... ... ... ...\n",
"2023-10-10 True False 0.588235\n",
"2023-10-11 False False 0.588235\n",
"2023-10-12 False False 0.588235\n",
"2023-10-13 False False 0.588235\n",
"2023-10-16 True False 0.571429\n",
"\n",
"[1332 rows x 3 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['H2Touch','H2Break','H2BreakTouch']]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-07-02 NaN\n",
"2018-07-03 NaN\n",
"2018-07-05 NaN\n",
"2018-07-06 NaN\n",
"2018-07-09 NaN\n",
" ... \n",
"2023-10-10 0.22\n",
"2023-10-11 0.21\n",
"2023-10-12 0.21\n",
"2023-10-13 0.21\n",
"2023-10-16 0.22\n",
"Name: H1BreakPct, Length: 1332, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['H1BreakPct']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res1, model1 = walk_forward_validation(df_final.dropna(axis=0), 'Target_clf', 100, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plot feature importances\n",
"plt.figure(figsize=(10, 12))\n",
"lgb.plot_importance(model1) # Adjust max_num_features as needed\n",
"plt.title(\"Feature Importances\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import yfinance as yf\n",
"\n",
"vix = yf.Ticker('^TNX')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vix.history(interval='30m')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_final.groupby(pd.qcut(df_final['CurrentGap'], 10))['Target_clf'].mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import roc_auc_score, precision_score, recall_score\n",
"# st.subheader('New Prediction')\n",
"nq = 7\n",
"\n",
"df_probas = res1.groupby(pd.qcut(res1['Predicted'],nq)).agg({'True':[np.mean,len,np.sum]})\n",
"# df_probas = res1.groupby(pd.cut(res1['Predicted'],[-np.inf, 0.27, 0.375, 0.625, 0.7, np.inf])).agg({'True':[np.mean,len,np.sum]})\n",
"df_probas.columns = ['PctGreen','NumObs','NumGreen']\n",
"\n",
"# Calculate quantiles\n",
"quantiles = pd.qcut(res1['Predicted'], nq, labels=False, duplicates='drop')\n",
"\n",
"# Determine the number of quantiles\n",
"num_quantiles = len(quantiles.unique())\n",
"\n",
"# Calculate the middle quantile(s)\n",
"if num_quantiles % 2 == 0: # Even number of quantiles\n",
" middle_quantiles = quantiles.isin([num_quantiles // 2 - 1, num_quantiles // 2])\n",
"else: # Odd number of quantiles\n",
" middle_quantiles = quantiles == num_quantiles // 2\n",
"\n",
"# Extract the lower and upper thresholds\n",
"lo_thres = 0.4 # res1.loc[middle_quantiles, 'Predicted'].min()\n",
"hi_thres = 0.6 # res1.loc[middle_quantiles, 'Predicted'].max()\n",
"\n",
"roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)\n",
"roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibPredicted')['True'].astype(int), res1.dropna(subset='CalibPredicted')['CalibPredicted'].values)\n",
"precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
"recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
"len_all = len(res1)\n",
"\n",
"res2_filtered = res1.loc[(res1['Predicted'] > hi_thres) | (res1['Predicted'] <= lo_thres)]\n",
"\n",
"roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)\n",
"roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibPredicted')['True'].astype(int), res2_filtered.dropna(subset='CalibPredicted')['CalibPredicted'].values)\n",
"precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
"recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
"len_hi = len(res2_filtered)\n",
"\n",
"df_performance = pd.DataFrame(\n",
" index=[\n",
" 'N',\n",
" 'ROC AUC',\n",
" 'Calib. AUC',\n",
" 'Precision',\n",
" 'Recall'\n",
" ],\n",
" columns = [\n",
" 'All',\n",
" 'High Confidence'\n",
" ],\n",
" data = [\n",
" [len_all, len_hi],\n",
" [roc_auc_score_all, roc_auc_score_hi],\n",
" [roc_auc_score_calib, roc_auc_score_hi_calib],\n",
" [precision_score_all, precision_score_hi],\n",
" [recall_score_all, recall_score_hi]\n",
" ]\n",
").round(2)\n",
"\n",
"def get_acc(t, p):\n",
" if t == False and p <= lo_thres:\n",
" return '✅' # ✅</p>\n",
" elif t == True and p > hi_thres:\n",
" return '✅' # \n",
" elif t == False and p > hi_thres:\n",
" return '❌' # ❌</p>\n",
" elif t == True and p <= lo_thres:\n",
" return '❌'\n",
" else:\n",
" return '🟨' # ⬜</p>\n",
" \n",
"def get_acc_html(t, p):\n",
" if t == False and p <= lo_thres:\n",
" return '✅'\n",
" elif t == True and p > hi_thres:\n",
" return '✅'\n",
" elif t == False and p > hi_thres:\n",
" return '❌'\n",
" elif t == True and p <= lo_thres:\n",
" return '❌'\n",
" else:\n",
" return '⬜'\n",
"\n",
"\n",
"perf_daily = res1.copy()\n",
"perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]\n",
"perf_daily['HTML'] = [get_acc_html(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"perf_daily.tail(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_probas.loc[df_probas.index[0], 'NumObs']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_levels = pd.DataFrame(\n",
" index=['H2','H1','L1','L2'],\n",
" columns=['Level','BreakPct(100)','TouchPct(100)'],\n",
" data=[\n",
" [f\"{data['H2'].iloc[-1]:.2f}\",f\"{data['H2BreakPct'].iloc[-2]:.1%}\",f\"{data['H2TouchPct'].iloc[-2]:.1%}\"],\n",
" [f\"{data['H1'].iloc[-1]:.2f}\",f\"{data['H1BreakPct'].iloc[-2]:.1%}\",f\"{data['H1TouchPct'].iloc[-2]:.1%}\"],\n",
" [f\"{data['L1'].iloc[-1]:.2f}\",f\"{data['L1BreakPct'].iloc[-2]:.1%}\",f\"{data['L1TouchPct'].iloc[-2]:.1%}\"],\n",
" [f\"{data['L2'].iloc[-1]:.2f}\",f\"{data['L2BreakPct'].iloc[-2]:.1%}\",f\"{data['L2TouchPct'].iloc[-2]:.1%}\"]\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_levels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.graph_objs as go\n",
"\n",
"plot_data = perf_daily.merge(data[['Open','High','Low','Close']], left_index=True, right_index=True)\n",
"\n",
"df = plot_data.copy()\n",
"\n",
"y_min = df['Low'].tail(50).min() - 50\n",
"y_max = df['High'].tail(50).max()\n",
"\n",
"increasing_color = '#3399ff' # Blue\n",
"decreasing_color = '#ff5f5f' # Red \n",
"\n",
"# Create a candlestick trace\n",
"candlestick_trace = go.Candlestick(\n",
" x=df.index,\n",
" open=df['Open'],\n",
" high=df['High'],\n",
" low=df['Low'],\n",
" close=df['Close'],\n",
" increasing_fillcolor=increasing_color, # Color for increasing candles\n",
" increasing_line_color=increasing_color, # Color for increasing candles\n",
" decreasing_fillcolor=decreasing_color, # Color for decreasing candles\n",
" decreasing_line_color=decreasing_color, # Color for decreasing candles\n",
" name='OHLC Chart'\n",
")\n",
"\n",
"# Create a scatter trace for symbols (correct and incorrect)\n",
"scatter_trace = go.Scatter(\n",
" x=df.index,\n",
" y=df['Low'] * 0.995,\n",
" text=df['HTML'],\n",
" mode='text',\n",
" marker=dict(size=10),\n",
" textposition='bottom center',\n",
" name='Predictions'\n",
")\n",
"\n",
"# Create a layout with initial x-axis range for the last 30 candles\n",
"layout = go.Layout(\n",
" title='OHLC Chart with Predictions (Right/Wrong)',\n",
" xaxis=dict(title='Date', range=[df.index[-50], df.index[-1]]), # Set initial range to last 30 data points\n",
" yaxis=dict(title='Price', range=[y_min, y_max]),\n",
" xaxis_rangeslider_visible=False,\n",
" template='plotly_dark'\n",
")\n",
"\n",
"# Create a figure\n",
"fig = go.Figure(data=[candlestick_trace, scatter_trace], layout=layout)\n",
"\n",
"fig.update_xaxes(\n",
" rangebreaks=[\n",
" # NOTE: Below values are bound (not single values), ie. hide x to y\n",
" dict(bounds=[\"sat\", \"mon\"]), # hide weekends, eg. hide sat to before mon\n",
" # dict(bounds=[16, 9.5], pattern=\"hour\"), # hide hours outside of 9.30am-4pm\n",
" # dict(values=[\"2019-12-25\", \"2020-12-24\"]) # hide holidays (Christmas and New Year's, etc)\n",
" ]\n",
" )\n",
"\n",
"\n",
"# Show the figure (you can also save it as an HTML file)\n",
"fig.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"XXX"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mplfinance as mpf\n",
"\n",
"# Sample data: assuming you already have df with OHLC data\n",
"# df['Date'] = df.index\n",
"# df.set_index('Date', inplace=True)\n",
"\n",
"# Determine the data range for the y-axis\n",
"# y_min = df['Low'].min()\n",
"# y_max = df['High'].max()\n",
"\n",
"df1 = df.tail(50)\n",
"\n",
"# Create a custom color map for increasing and decreasing candles\n",
"mc = mpf.make_marketcolors(\n",
" up='#3399ff', # Blue for increasing candles\n",
" down='#ff5f5f', # Red for decreasing candles\n",
" inherit=True\n",
")\n",
"\n",
"s = mpf.make_mpf_style(marketcolors=mc,\n",
" gridcolor='#333333', # Set grid color to match dark background\n",
" facecolor='#222222', # Set background color to match dark background\n",
" edgecolor='#222222' # Set edge color to match dark background)\n",
")\n",
"\n",
"correct = np.where(df1['HTML']=='✅',1, np.nan) * df1['Low'] * 0.997\n",
"mids = np.where(df1['HTML']=='⬜',1, np.nan) * df1['Low'] * 0.997\n",
"wrongs = np.where(df1['HTML']=='❌',1, np.nan) * df1['Low'] * 0.997\n",
"\n",
"\n",
"# Create an addplot for annotations\n",
"add_plot = mpf.make_addplot(correct, color='green', sc='green')\n",
"add_mids = mpf.make_addplot(mids, color='yellow', marker='green')\n",
"add_wrongs = mpf.make_addplot(wrongs, color='red', marker='green')\n",
"\n",
"# Create an OHLC chart with mplfinance\n",
"mpf.plot(df1, type='candle', style='binance', title='OHLC Chart with Predictions (Right/Wrong)', ylabel='Price',\n",
" # hlines=dict(hlines=[y_min, y_max], colors=['gray', 'gray']), # Add horizontal lines for y-axis range\n",
" xrotation=0, # Rotate x-axis labels if needed\n",
" figratio=(10, 6), # Adjust the figure size as needed\n",
" volume=False,\n",
" addplot = [add_plot, add_mids, add_wrongs]) # Disable volume bars\n",
"\n",
"\n",
"# Show the chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mpf.available_fonts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"perf_daily1 = perf_daily.merge(data['ClosePct'], left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res2 = res1.merge(data[['ClosePct','HighPct','LowPct']], left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']\n",
"df_probas = res2.groupby(pd.qcut(res2['Predicted'],7)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean, np.std], 'HighPct':[np.mean], 'LowPct':[np.mean]})\n",
"# df_probas = res2.groupby(pd.cut(res2['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean], 'HighPct':[np.mean], 'LowPct':[np.mean]})\n",
"df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf','PerfStD','AvgHigh','AvgLow']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_probas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_predicted_value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res2['Quantile'] = pd.cut(res2['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Assuming you have a DataFrame 'res2' with the columns 'Quantile' and 'ClosePct'\n",
"# Assuming you have a list 'int_labels' containing the unique values for 'Quantile'\n",
"\n",
"# Create a 2x3 grid of subplots\n",
"fig, axs = plt.subplots(2, 3, figsize=(15, 8))\n",
"\n",
"# Loop through the 'int_labels' and plot the histograms in each subplot\n",
"for i, lbl in enumerate(int_labels):\n",
" # Get the subplot position based on the index i\n",
" row = i // 3\n",
" col = i % 3\n",
" \n",
" # Filter the DataFrame based on the specified value\n",
" data_subset = res2.loc[res2['Quantile'] == lbl, 'LowPct']\n",
" \n",
" # Plot the histogram in the corresponding subplot\n",
" axs[row, col].hist(data_subset)\n",
" axs[row, col].set_title(lbl)\n",
"\n",
"# Add some space between the subplots\n",
"plt.tight_layout()\n",
"\n",
"# Show the plot\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Investigate EM\n",
"data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))\n",
"data['VIX_EM_High'] = data['Close'] + data['VIX_EM']\n",
"data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']\n",
"\n",
"data['VIX_EM_125'] = data['VIX_EM'] * 1.25\n",
"data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']\n",
"data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']\n",
"\n",
"data['VIX_EM_15'] = data['VIX_EM'] * 1.5\n",
"data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']\n",
"data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']\n",
"\n",
"data['VIX_EM'] = data['VIX_EM'].shift(1)\n",
"data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)\n",
"data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)\n",
"\n",
"data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)\n",
"data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)\n",
"data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)\n",
"\n",
"data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)\n",
"data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)\n",
"data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data[['VIX_EM','VIX_EM_15','VIX_EM_15_High','Close']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often did price close within EM?\n",
"len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often was EM tested?\n",
"len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often did price close within EM?\n",
"len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often was EM tested?\n",
"len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often did price close within EM?\n",
"len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# How often was EM tested?\n",
"len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"file_extension": ".py",
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|