├── data └── images │ ├── outliers.png │ ├── prob_lr.png │ ├── prob_logr.png │ ├── roc_sklearn.png │ ├── TrendStrength.png │ ├── ema optimize.png │ ├── ema optimized.png │ ├── model_results.png │ ├── priceranget1.png │ ├── priceranget2.png │ ├── roc_data_hist.png │ ├── trend_period.png │ ├── VolumeBarSamples.png │ ├── gmm_mixture_data.png │ ├── roc_mixture_pred.png │ ├── trend_pred_mixture.png │ ├── Mixture model components .png │ └── predicted trend from mixture model.png ├── utils ├── __pycache__ │ └── utils.cpython-38.pyc └── utils.py ├── README.md └── sampling_and_annotation.ipynb /data/images/outliers.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kaneelgit/ML-Quant/HEAD/data/images/outliers.png -------------------------------------------------------------------------------- /data/images/prob_lr.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/kaneelgit/ML-Quant/HEAD/data/images/predicted trend from mixture model.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ML-Quant 2 | Notebooks and Code for ML based quant strategies and research. 3 | 4 | ## Level 2 Visualization (Nasdaq Micro Futures) 5 | ![Screenshot 2025-08-04 001500](https://github.com/user-attachments/assets/248f14f1-2343-455c-a4b0-f1a0c669a6ef) 6 | 7 | ## Annotations and Sampling 8 | 9 | ### Trend prediction using a Gaussian Mixture Model 10 | Original data distribution, prediction by a mixture distribution implemented using tensorflow probability(TFP) and the prediction by sklearn Gaussian Mixture Model. 11 | ![gmm_mixture_data](https://github.com/kaneelgit/ML-Quant/assets/85404022/f6b0b76d-8f23-4626-8e0c-71a55a721af8) 12 | 13 | TFP mixture model components 14 | 15 | ![Mixture model components ](https://github.com/kaneelgit/ML-Quant/assets/85404022/8934afc3-7cf7-4e2f-a75e-d166a1e98401) 16 | 17 | Predicted trends by the mixture model 18 | ![predicted trend from mixture model](https://github.com/kaneelgit/ML-Quant/assets/85404022/926c70e7-8cc2-40f1-a7f1-5d8c06eb37cf) 19 | 20 | ### Volume bar sampling 21 | Here we sample and plot 'close' value based on the volume. (Close value at every "xx" volume) 22 | ![VolumeBarSamples](https://github.com/kaneelgit/ML-Quant/assets/85404022/79adf80e-8f49-4cfa-aed0-c845aab7d052) 23 | 24 | ### Trend strength and trend period 25 | Here we generate trends (% increase or decrease in SPY) and find the trend strength (value between 0 and 1 depending on how long the trend last) and trend period (how long the trend last). 26 | ![TrendStrength](https://github.com/kaneelgit/ML-Quant/assets/85404022/a11d9f7f-ee22-4dfa-86e8-67c8f94ba04f) 27 | ![trend_period](https://github.com/kaneelgit/ML-Quant/assets/85404022/a3b697b5-09d4-43aa-ae6d-32b7325dbf26) 28 | 29 | 30 | ## EMA Optimization 31 | This notebook has a method to find the best EMA lines that represent 'uptrends' and 'downtrends' in S&P 500. 32 | 33 | ![ema optimize](https://github.com/kaneelgit/ML-Quant/assets/85404022/5eda7ba5-bec7-4602-b0c8-bf9742ffaa1b) 34 | 35 | ![ema optimized](https://github.com/kaneelgit/ML-Quant/assets/85404022/e8579eab-7cb6-478d-b9e5-0b52580ed130) 36 | 37 | ## Probabilistic Logistic Regression 38 | This shows how to use probabilistic Logistic Regression to detect outliers in a dataset. Prob LR is useful to guage the confidence of a decision. This is helpful to decide the risk of a bet. 39 | 40 | ![prob_logr](https://github.com/kaneelgit/ML-Quant/assets/85404022/bd3dddaf-4364-49b6-8ad0-9c9132ddd981) 41 | 42 | ![model_results](https://github.com/kaneelgit/ML-Quant/assets/85404022/1429866a-553a-42c7-84ed-198e92f2cd23) 43 | 44 | ## Probabilistic Linear Regression 45 | 46 | ![priceranget1](https://github.com/kaneelgit/ML-Quant/assets/85404022/b835b6d6-af3f-45a0-81d5-2d9cbdb07d57) 47 | 48 | ![priceranget2](https://github.com/kaneelgit/ML-Quant/assets/85404022/704b4cb0-8452-4d3a-b2d5-c8ff10549c84) 49 | 50 | ![prob_lr](https://github.com/kaneelgit/ML-Quant/assets/85404022/c3069aa7-67b4-420c-a595-71eb2b689a9a) 51 | 52 | ## Unsupervised Buy-Sell detection 53 | 54 | ![outliers](https://user-images.githubusercontent.com/85404022/219976291-3b833654-fa04-4009-ae5f-f7139881732e.png) 55 | 56 | 57 | -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Author - Kaneel Senevirathne 3 | Date - 2/19/2023 4 | ''' 5 | 6 | from scipy.signal import argrelextrema 7 | import yfinance as yf 8 | import numpy as np 9 | import pandas as pd 10 | 11 | 12 | def get_data_yf(symbol, start, end = None): 13 | """ 14 | Get data from yahoo finance given symbol, start date and end date. 15 | """ 16 | return yf.download(symbol, start, end) 17 | 18 | def normalized_values(high, low, close): 19 | """ 20 | normalize the price between 0 and 1. 21 | """ 22 | #epsilon to avoid deletion by 0 23 | epsilon = 10e-10 24 | 25 | #subtract the lows 26 | high = high - low 27 | close = close - low 28 | 29 | return close/(high + epsilon) 30 | 31 | def create_targets(data, n = 10, target_winloss = [20, 0]): 32 | ''' 33 | Use local min and maxes to create targets given n 34 | ''' 35 | data = data.copy().reset_index(drop = False) 36 | 37 | #function to find how many were correct 38 | def locmin(row, close, target_win, target_loss): 39 | 40 | if (row.name + 1) == len(data): 41 | return pd.Series({'min': 0, 'min_value': close}) 42 | 43 | else: 44 | 45 | df = data[row.name + 1:].reset_index() 46 | win = row.close + target_win 47 | stop = row.close - target_loss 48 | 49 | val = df.loc[(df['close'] >= win) | (df['close'] <= stop)] 50 | 51 | if len(val) == 0: 52 | return pd.Series({'min': 0, 'min_value': close}) 53 | else: 54 | v = val.iloc[0]['close'] 55 | 56 | if v <= stop: 57 | out = 0 58 | if v >= win: 59 | out = 1 60 | 61 | return pd.Series({'min': out, 'min_value': close}) 62 | 63 | def locmax(row, close, target_win, target_loss): 64 | 65 | if (row.name + 1) == len(data): 66 | return pd.Series({'max': 0, 'max_value': close}) 67 | 68 | else: 69 | df = data[row.name + 1:].reset_index() 70 | win = row.close - target_win 71 | stop = row.close + target_loss 72 | 73 | val = df.loc[(df['close'] <= win) | (df['close'] >= stop)] 74 | 75 | if len(val) == 0: 76 | return pd.Series({'max': 0, 'max_value': close}) 77 | 78 | else: 79 | v = val.iloc[0]['close'] 80 | 81 | if v <= win: 82 | out = 1 83 | if v >= stop: 84 | out = 0 85 | 86 | return pd.Series({'max': out, 'max_value': close}) 87 | 88 | 89 | data['loc_min'] = data.iloc[argrelextrema(data.close.values, np.less_equal, order = n)[0]]['close'] 90 | data['loc_max'] = data.iloc[argrelextrema(data.close.values, np.greater_equal, order = n)[0]]['close'] 91 | 92 | #get locmins and loc maxes 93 | data[['min', 'min_value']] = data.apply(lambda x: locmin(x, x.close, target_winloss[0], target_winloss[1]), axis = 1) 94 | data[['max', 'max_value']] = data.apply(lambda x: locmax(x, x.close, target_winloss[0], target_winloss[1]), axis = 1) 95 | 96 | data['target'] = [1 if (x == 1) and (a > 0) else 2 if (y == 1) and (b > 0) else 0 for x, y, a, b in \ 97 | zip(data['min'], data['max'], data['loc_min'], data['loc_max'])] 98 | 99 | return data 100 | 101 | def create_volume_bar(data, thresh = 2000): 102 | 103 | df = data.copy() 104 | 105 | #create volume bars 106 | df_resampled = df.resample('1T', on='date').agg({'open': 'first', 'high': 'max', 'close': 'last', 'low': 'min', 'volume': 'sum'}).fillna(0).reset_index() 107 | 108 | # Create a new dataframe to store the resampled values when volume accumulates 1000 units 109 | df_new = pd.DataFrame(columns=['open', 'high', 'close', 'low', 'delta', 'o_index', 'date']) 110 | 111 | #volume threshold 112 | vol_thresh = thresh 113 | 114 | #initial values 115 | volume_accumulated = 0 116 | start_time = None 117 | 118 | # Iterate over each row in the resampled dataframe 119 | for index, row in df_resampled.iterrows(): 120 | volume_accumulated += row['volume'] 121 | 122 | if volume_accumulated >= vol_thresh: 123 | # Calculate the time difference since the last 1000 volume accumulation 124 | delta = (index - start_time) if start_time else 0 125 | 126 | # Append a new row to the new dataframe 127 | df_new.loc[index] = [row['open'], row['high'], row['close'], row['low'], delta, index, row['date']] 128 | 129 | # Reset the volume accumulator and start time for the next accumulation 130 | volume_accumulated = 0 131 | start_time = None 132 | 133 | if volume_accumulated < vol_thresh and start_time is None: 134 | # Set the start time when the volume accumulation starts 135 | start_time = index 136 | 137 | return df_new 138 | 139 | def generate_trends(data): 140 | 141 | data['trend'] = 0 142 | data['trend_strength'] = np.nan 143 | data['trend_period'] = np.nan 144 | 145 | targets = data['target'].values 146 | 147 | min_indices = np.where(targets == 1)[0] 148 | 149 | max_indices = np.where(targets == 2)[0] 150 | 151 | ind_dict = {} 152 | 153 | for i in min_indices: 154 | ind_dict[i] = 1 155 | 156 | for i in max_indices: 157 | ind_dict[i] = 2 158 | 159 | sorted_dict = {k: ind_dict[k] for k in sorted(ind_dict)} 160 | 161 | current_ind = next(iter(sorted_dict)) 162 | current_trend = sorted_dict[current_ind] 163 | 164 | 165 | for i, trend in sorted_dict.items(): 166 | 167 | if current_trend == trend: 168 | continue 169 | elif (current_trend == 1) and (trend == 2): 170 | data.loc[current_ind:i, 'trend'] = 1 171 | 172 | trend_strength = 1 - ((np.arange(current_ind, i, 1) - current_ind) / (i - current_ind)) 173 | 174 | data.loc[np.arange(current_ind, i, 1), 'trend_strength'] = trend_strength.tolist() 175 | 176 | data.loc[current_ind:i, 'trend_period'] = (i - current_ind) 177 | 178 | current_ind = i 179 | current_trend = trend 180 | elif (current_trend == 2) and (trend == 1): 181 | data.loc[current_ind:i, 'trend'] = 2 182 | 183 | trend_strength = 1 - ((np.arange(current_ind, i, 1) - current_ind) / (i - current_ind)) 184 | 185 | data.loc[np.arange(current_ind, i, 1), 'trend_strength'] = trend_strength.tolist() 186 | 187 | data.loc[current_ind:i, 'trend_period'] = (i - current_ind) 188 | 189 | current_ind = i 190 | current_trend = trend 191 | 192 | return data -------------------------------------------------------------------------------- /sampling_and_annotation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "da2265be", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import yfinance as yf\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import matplotlib.font_manager\n", 13 | "import pandas as pd\n", 14 | "import numpy as np\n", 15 | "import seaborn as sns\n", 16 | "from scipy import stats\n", 17 | "from datetime import datetime, timedelta\n", 18 | "\n", 19 | "#sklearn functions\n", 20 | "from sklearn.preprocessing import MinMaxScaler\n", 21 | "from sklearn.decomposition import PCA\n", 22 | "from sklearn.covariance import EllipticEnvelope\n", 23 | "from sklearn.svm import OneClassSVM\n", 24 | "\n", 25 | "from utils import utils\n", 26 | "import matplotlib.patches as mpatches \n", 27 | "\n", 28 | "import warnings\n", 29 | "\n", 30 | "warnings.filterwarnings(\"ignore\")" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "id": "fef1eb54", 36 | "metadata": {}, 37 | "source": [ 38 | "## Volume Bar Sampling" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 47, 44 | "id": "e0ea285c", 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "name": "stdout", 49 | "output_type": "stream", 50 | "text": [ 51 | "[*********************100%***********************] 1 of 1 completed\n" 52 | ] 53 | }, 54 | { 55 | "data": { 56 | "image/png": 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\n", 57 | "text/plain": [ 58 | "
" 59 | ] 60 | }, 61 | "metadata": { 62 | "needs_background": "light" 63 | }, 64 | "output_type": "display_data" 65 | } 66 | ], 67 | "source": [ 68 | "start_date = \"2023-04-22\"\n", 69 | "end_date = \"2023-10-22\"\n", 70 | "\n", 71 | "#downlaod data\n", 72 | "df = yf.download('SPY', start = start_date, end = end_date, interval = '1h')\n", 73 | "\n", 74 | "#reset index\n", 75 | "df = df.reset_index()\n", 76 | "\n", 77 | "#rename the index column to Date\n", 78 | "df.rename(columns = {'index': 'Date'}, inplace = True)\n", 79 | "\n", 80 | "orig_columns = list(df.columns)\n", 81 | "lower_orig_columns = [x.lower() for x in orig_columns]\n", 82 | "\n", 83 | "for i, column_name in enumerate(lower_orig_columns):\n", 84 | " df.rename(columns = {f'{orig_columns[i]}': f'{column_name}'}, inplace = True)\n", 85 | "\n", 86 | "\n", 87 | "#plot the results\n", 88 | "plt.figure(figsize = (15, 5))\n", 89 | "plt.plot(df['date'], df['close'])\n", 90 | "plt.show()" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "id": "617714a5", 96 | "metadata": {}, 97 | "source": [ 98 | "Find a volume threshold to sample. Here we will use (mean * 10) as the threshold to sample the 'open', 'high', 'close', 'low' values when everytime SPY hits the volume threshold. The function 'create_volume_bar' below returns a dataframe with volume bars." 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 35, 104 | "id": "f1e334d1", 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "def create_volume_bar(data, thresh = 2000):\n", 109 | " \n", 110 | " df = data.copy()\n", 111 | " \n", 112 | " #create volume bars\n", 113 | " df_resampled = df.resample('1T', on='date').agg({'open': 'first', 'high': 'max', 'close': 'last', 'low': 'min', 'volume': 'sum'}).fillna(0).reset_index()\n", 114 | "\n", 115 | " # Create a new dataframe to store the resampled values when volume accumulates 1000 units\n", 116 | " df_new = pd.DataFrame(columns=['open', 'high', 'close', 'low', 'delta', 'o_index', 'date'])\n", 117 | "\n", 118 | " #volume threshold\n", 119 | " vol_thresh = thresh\n", 120 | "\n", 121 | " #initial values\n", 122 | " volume_accumulated = 0\n", 123 | " start_time = None\n", 124 | "\n", 125 | " # Iterate over each row in the resampled dataframe\n", 126 | " for index, row in df_resampled.iterrows():\n", 127 | " volume_accumulated += row['volume']\n", 128 | "\n", 129 | " if volume_accumulated >= vol_thresh:\n", 130 | " # Calculate the time difference since the last 1000 volume accumulation\n", 131 | " delta = (index - start_time) if start_time else 0\n", 132 | "\n", 133 | " # Append a new row to the new dataframe\n", 134 | " df_new.loc[index] = [row['open'], row['high'], row['close'], row['low'], delta, index, row['date']]\n", 135 | "\n", 136 | " # Reset the volume accumulator and start time for the next accumulation\n", 137 | " volume_accumulated = 0\n", 138 | " start_time = None\n", 139 | "\n", 140 | " if volume_accumulated < vol_thresh and start_time is None:\n", 141 | " # Set the start time when the volume accumulation starts\n", 142 | " start_time = index\n", 143 | " \n", 144 | " return df_new" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 36, 150 | "id": "09d98738", 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "#mean volume\n", 155 | "mean_volume = df['volume'].mean() * 10\n", 156 | "\n", 157 | "#sample volume bar \n", 158 | "data_vb = create_volume_bar(df, mean_volume)" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 37, 164 | "id": "ea48a875", 165 | "metadata": {}, 166 | "outputs": [ 167 | { 168 | "data": { 169 | "image/png": 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\n", 170 | "text/plain": [ 171 | "
" 172 | ] 173 | }, 174 | "metadata": { 175 | "needs_background": "light" 176 | }, 177 | "output_type": "display_data" 178 | } 179 | ], 180 | "source": [ 181 | "#plot the results\n", 182 | "plt.figure(figsize = (15, 5))\n", 183 | "plt.title('6 Month SPY Data')\n", 184 | "plt.plot(df['date'], df['close'], label = '1 Hour Close')\n", 185 | "plt.scatter(data_vb['date'], data_vb['close'], c = 'k', label = 'Volume Bar Samples')\n", 186 | "plt.xlabel('Date')\n", 187 | "plt.ylabel('Price')\n", 188 | "plt.legend()\n", 189 | "plt.show()" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "id": "a7ee49dc", 195 | "metadata": {}, 196 | "source": [ 197 | "# Annotations\n", 198 | "\n", 199 | "### Trend Annotation\n", 200 | "\n", 201 | "Below is a function to generate trends. We will create targets to identify SPY increasing and decreasing a certain value to create trends. We Will also create the trend strength and trend period." 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 48, 207 | "id": "c0654710", 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "#create targets when SPY goes up 5 points\n", 212 | "df = utils.create_targets(df, target_winloss = [5, 0])" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 49, 218 | "id": "47dceca6", 219 | "metadata": {}, 220 | "outputs": [], 221 | "source": [ 222 | "#generate trend, strength and period\n", 223 | "def generate_trends(data):\n", 224 | " \n", 225 | " data['trend'] = 0\n", 226 | " data['trend_strength'] = np.nan\n", 227 | " data['trend_period'] = np.nan\n", 228 | " \n", 229 | " targets = data['target'].values\n", 230 | "\n", 231 | " min_indices = np.where(targets == 1)[0]\n", 232 | "\n", 233 | " max_indices = np.where(targets == 2)[0]\n", 234 | "\n", 235 | " ind_dict = {}\n", 236 | "\n", 237 | " for i in min_indices:\n", 238 | " ind_dict[i] = 1\n", 239 | "\n", 240 | " for i in max_indices:\n", 241 | " ind_dict[i] = 2\n", 242 | "\n", 243 | " sorted_dict = {k: ind_dict[k] for k in sorted(ind_dict)}\n", 244 | "\n", 245 | " current_ind = next(iter(sorted_dict))\n", 246 | " current_trend = sorted_dict[current_ind]\n", 247 | "\n", 248 | "\n", 249 | " for i, trend in sorted_dict.items():\n", 250 | "\n", 251 | " if current_trend == trend:\n", 252 | " continue\n", 253 | " elif (current_trend == 1) and (trend == 2):\n", 254 | " data.loc[current_ind:i, 'trend'] = 1\n", 255 | " \n", 256 | " trend_strength = 1 - ((np.arange(current_ind, i, 1) - current_ind) / (i - current_ind))\n", 257 | " \n", 258 | " data.loc[np.arange(current_ind, i, 1), 'trend_strength'] = trend_strength.tolist()\n", 259 | " \n", 260 | " data.loc[current_ind:i, 'trend_period'] = (i - current_ind)\n", 261 | " \n", 262 | " current_ind = i\n", 263 | " current_trend = trend\n", 264 | " elif (current_trend == 2) and (trend == 1):\n", 265 | " data.loc[current_ind:i, 'trend'] = 2\n", 266 | " \n", 267 | " trend_strength = 1 - ((np.arange(current_ind, i, 1) - current_ind) / (i - current_ind))\n", 268 | " \n", 269 | " data.loc[np.arange(current_ind, i, 1), 'trend_strength'] = trend_strength.tolist()\n", 270 | " \n", 271 | " data.loc[current_ind:i, 'trend_period'] = (i - current_ind)\n", 272 | " \n", 273 | " current_ind = i\n", 274 | " current_trend = trend\n", 275 | " \n", 276 | " return data" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": 50, 282 | "id": "0c4b593e", 283 | "metadata": {}, 284 | "outputs": [], 285 | "source": [ 286 | "#generate trends\n", 287 | "df_trend = generate_trends(df)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 57, 293 | "id": "19df2f4a", 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "
" 300 | ] 301 | }, 302 | "metadata": {}, 303 | "output_type": "display_data" 304 | }, 305 | { 306 | "data": { 307 | "image/png": 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\n", 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" 310 | ] 311 | }, 312 | "metadata": { 313 | "needs_background": "light" 314 | }, 315 | "output_type": "display_data" 316 | } 317 | ], 318 | "source": [ 319 | "#plot trend strength and period\n", 320 | "plt.figure(figsize = (15, 3))\n", 321 | "\n", 322 | "data_plot = df_trend.copy()\n", 323 | "colors = {0: 'yellow', 1: 'green', 2: 'red'}\n", 324 | "\n", 325 | "# Create a colormap from light red to dark red\n", 326 | "colormap = plt.get_cmap('Reds')\n", 327 | "colormap_g = plt.get_cmap('Greens')\n", 328 | "\n", 329 | "# Normalize trend_strength values to the colormap\n", 330 | "norm = plt.Normalize(data_plot['trend_strength'].min(), data_plot['trend_strength'].max())\n", 331 | "\n", 332 | "# Create a ScalarMappable for color mapping\n", 333 | "sm = plt.cm.ScalarMappable(cmap=colormap, norm=norm)\n", 334 | "sm.set_array([])\n", 335 | "\n", 336 | "sm_g = plt.cm.ScalarMappable(cmap=colormap_g, norm=norm)\n", 337 | "sm_g.set_array([])\n", 338 | "\n", 339 | "\n", 340 | "plt.figure(figsize = (15, 5))\n", 341 | "plt.plot(data_plot['date'], data_plot['close'], alpha = 0.2)\n", 342 | "plt.scatter(data_plot[data_plot['trend'] == 2]['date'], data_plot[data_plot['trend'] == 2]['close'], c=colormap(norm(data_plot[data_plot['trend'] == 2]['trend_strength'])), cmap=colormap, s = 10)\n", 343 | "plt.scatter(data_plot[data_plot['trend'] == 1]['date'], data_plot[data_plot['trend'] == 1]['close'], c=colormap_g(norm(data_plot[data_plot['trend'] == 1]['trend_strength'])), cmap=colormap_g, s = 10)\n", 344 | "plt.title('Trend Strength')\n", 345 | "plt.xlabel('Date')\n", 346 | "plt.ylabel('Close')\n", 347 | "plt.show()" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": 63, 353 | "id": "b81a3c0b", 354 | "metadata": {}, 355 | "outputs": [ 356 | { 357 | "data": { 358 | "image/png": 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" 361 | ] 362 | }, 363 | "metadata": { 364 | "needs_background": "light" 365 | }, 366 | "output_type": "display_data" 367 | } 368 | ], 369 | "source": [ 370 | "#plot trend period\n", 371 | "# # cont_data['date'] = cont_data['o_index']\n", 372 | "data_plot = df_trend.copy()\n", 373 | "colors = {0: 'yellow', 1: 'green', 2: 'red'}\n", 374 | "plt.figure(figsize = (15, 5))\n", 375 | "plt.scatter(data_plot['date'], data_plot['trend_period'], c = data_plot['trend'].apply(lambda x: colors[x]), label = 'Trend Direction Down')\n", 376 | "plt.bar(data_plot['date'], data_plot['trend_period'])\n", 377 | "plt.xlabel('Date')\n", 378 | "plt.ylabel('Trend Period')\n", 379 | "plt.legend()\n", 380 | "plt.show()" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": null, 386 | "id": "dd6170e8", 387 | "metadata": {}, 388 | "outputs": [], 389 | "source": [] 390 | } 391 | ], 392 | "metadata": { 393 | "kernelspec": { 394 | "display_name": "Python 3", 395 | "language": "python", 396 | "name": "python3" 397 | }, 398 | "language_info": { 399 | "codemirror_mode": { 400 | "name": "ipython", 401 | "version": 3 402 | }, 403 | "file_extension": ".py", 404 | "mimetype": "text/x-python", 405 | "name": "python", 406 | "nbconvert_exporter": "python", 407 | "pygments_lexer": "ipython3", 408 | "version": "3.8.8" 409 | } 410 | }, 411 | "nbformat": 4, 412 | "nbformat_minor": 5 413 | } 414 | --------------------------------------------------------------------------------