├── .gitignore ├── requirements.txt ├── 200d.ma_results.png ├── Results ├── LR │ ├── LR_Raw.png │ └── LR_standard.png ├── FRED │ ├── FRED1.png │ ├── FRED2.png │ ├── FRED3.png │ └── strategyresults.png ├── MA │ └── MA_cross_all.png └── MACD │ ├── MACD_AAPL_1h.png │ ├── MACD_AMZN_1h.png │ ├── MACD_MSFT_1h.png │ ├── MACD_NVDA_1h.png │ ├── MACD_NVDA_1m.png │ ├── MACD_SPY1m.png │ ├── MACD_SPY_1d.png │ ├── MACD_SPY_1h.png │ ├── MACD_SPY_1m.png │ ├── MACD_SPY_5m.png │ ├── MACD_TSLA_1d.png │ ├── MACD_TSLA_1h.png │ ├── MACD_TSLA_1m.png │ ├── MACD_TSLA_5m.png │ └── MACD_UNH_1h.png ├── __pycache__ └── fred_api_key.cpython-313.pyc ├── README.md ├── FVG.ipynb ├── StandardDev.ipynb ├── Regression.ipynb └── MA.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | /myenv 2 | animate.py 3 | fred_api_key.py -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | yfinance 3 | numpy 4 | matplotlib 5 | jupyter 6 | numpy -------------------------------------------------------------------------------- /200d.ma_results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/n84d/SharpEducation/HEAD/200d.ma_results.png -------------------------------------------------------------------------------- /Results/LR/LR_Raw.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/n84d/SharpEducation/HEAD/Results/LR/LR_Raw.png -------------------------------------------------------------------------------- /Results/FRED/FRED1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/n84d/SharpEducation/HEAD/Results/FRED/FRED1.png 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-------------------------------------------------------------------------------- /__pycache__/fred_api_key.cpython-313.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/n84d/SharpEducation/HEAD/__pycache__/fred_api_key.cpython-313.pyc -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SharpEducation 2 | A repository for educational contant on the Sharp Research channel 3 | 4 | once you load up the folder, open your terminal 5 | 6 | run this command: 7 | 8 | python -m venv myenv 9 | 10 | you can change the name to anything you want, but myenv is standard 11 | 12 | assuming your environment name is myenv, run this command in your terminal 13 | 14 | myenv\Scripts\Activate 15 | 16 | once your virtual environment is activated, run pip install -r requirements.txt 17 | 18 | you now have all the libraries necessary to tweak the code provided in the repository! 19 | 20 | adjust the global variables to see how different strategies perform 21 | -------------------------------------------------------------------------------- /FVG.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 18, 6 | "id": "2cb8f1e1", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import yfinance as yf\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import pandas as pd\n", 13 | "import numpy as np" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 19, 19 | "id": "4acba3a7", 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stderr", 24 | "output_type": "stream", 25 | "text": [ 26 | "[*********************100%***********************] 1 of 1 completed\n" 27 | ] 28 | }, 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "1658 Bullish FVGs\n", 34 | "Bull FVG 5 Period Average returns: 0.1%\n", 35 | "Bull FVG Win Rate: 42.28%\n", 36 | "828 Medium Sized Bullish FVGs\n", 37 | "Medium Bull FVG 5 Period Average returns: 0.08%\n", 38 | "Medium Bull FVG Win Rate: 44.69%\n", 39 | "1010 Bearish FVGS\n", 40 | "Bear FVG 5 Period Average returns: 0.45%\n", 41 | "Bear FVG Win Rate: 61.09%\n", 42 | "504 Medium Sized Bearish FVGs\n", 43 | "Medium Bear FVG 5 Period Average returns: 0.37%\n", 44 | "Medium Bull FVG Win Rate: 59.52%\n" 45 | ] 46 | }, 47 | { 48 | "data": { 49 | "image/png": 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", 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", 60 | "text/plain": [ 61 | "
" 62 | ] 63 | }, 64 | "metadata": {}, 65 | "output_type": "display_data" 66 | } 67 | ], 68 | "source": [ 69 | "TICKER = 'SPY'\n", 70 | "LOOKBACK = 10000\n", 71 | "HOLDING_PERIOD = 5\n", 72 | "\n", 73 | "def get_data(ticker=TICKER):\n", 74 | " df = yf.download(ticker)\n", 75 | " df.columns = df.columns.get_level_values(0)\n", 76 | "\n", 77 | " # only return the subset of data you are interested in\n", 78 | " return df.iloc[-LOOKBACK:, :]\n", 79 | "\n", 80 | "def bull_fvg(df):\n", 81 | "\n", 82 | " df['High_2prev'] = df['High'].shift(2)\n", 83 | " df['Bull_FVG'] = (df['Low'] > df['High_2prev']).astype(int)\n", 84 | " df['Bull_FVG_Val'] = (df['Low'] - df['High_2prev']) * df['Bull_FVG'] / df['Close']\n", 85 | "\n", 86 | " fvg_subset = df[df['Bull_FVG_Val'] > 0]\n", 87 | "\n", 88 | " plt.hist(fvg_subset['Bull_FVG_Val'], bins=10)\n", 89 | " plt.title('Bullish Fair Value Gap Values')\n", 90 | "\n", 91 | " return df\n", 92 | "\n", 93 | "def bear_fvg(df):\n", 94 | "\n", 95 | " df['Low_2prev'] = df['Low'].shift(2)\n", 96 | " df['Bear_FVG'] = (df['High'] < df['Low_2prev']).astype(int)\n", 97 | " df['Bear_FVG_Val'] = (df['High'] - df['Low_2prev']) * df['Bear_FVG'] / df['Close']\n", 98 | "\n", 99 | " fvg_subset = df[df['Bear_FVG_Val'] < 0]\n", 100 | "\n", 101 | " plt.figure()\n", 102 | " plt.hist(fvg_subset['Bear_FVG_Val'], bins=10)\n", 103 | " plt.title('Bearish Fair Value Gap Values')\n", 104 | "\n", 105 | " return df\n", 106 | "\n", 107 | "def assess_bull_FVG(df, holding_period=HOLDING_PERIOD):\n", 108 | "\n", 109 | " #5 day holding period returns\n", 110 | " df[f'Returns_In_{holding_period}_Periods'] = df['Close'].shift(-holding_period) / df['Close']\n", 111 | " \n", 112 | " fvg_subset = df[df['Bull_FVG_Val'] > 0]\n", 113 | " print(f'{len(fvg_subset)} Bullish FVGs')\n", 114 | " print(f'Bull FVG {holding_period} Period Average returns: {round((fvg_subset[f'Returns_In_{holding_period}_Periods'].mean() - 1) * 100, 2)}%')\n", 115 | "\n", 116 | " win_rate = (fvg_subset[f'Returns_In_{holding_period}_Periods'] < 1).mean() * 100\n", 117 | " print(f'Bull FVG Win Rate: {round(win_rate, 2)}%')\n", 118 | "\n", 119 | " # define quantiles\n", 120 | " lower = fvg_subset['Bull_FVG_Val'].quantile(.25)\n", 121 | " upper = fvg_subset['Bull_FVG_Val'].quantile(.75)\n", 122 | "\n", 123 | " # subset\n", 124 | " fvg_medium_val = fvg_subset[\n", 125 | " (fvg_subset['Bull_FVG_Val'] >= lower) & (fvg_subset['Bull_FVG_Val'] <= upper)\n", 126 | " ]\n", 127 | " print(f'{len(fvg_medium_val)} Medium Sized Bullish FVGs')\n", 128 | " print(f'Medium Bull FVG {holding_period} Period Average returns: {round((fvg_medium_val[f'Returns_In_{holding_period}_Periods'].mean() - 1) * 100, 2)}%')\n", 129 | "\n", 130 | " win_rate_medium = (fvg_medium_val[f'Returns_In_{holding_period}_Periods'] < 1).mean() * 100\n", 131 | " print(f'Medium Bull FVG Win Rate: {round(win_rate_medium, 2)}%')\n", 132 | "\n", 133 | " return df\n", 134 | "\n", 135 | "def assess_bear_FVG(df, holding_period=HOLDING_PERIOD):\n", 136 | "\n", 137 | " #5 day holding period returns\n", 138 | " df[f'Returns_In_{holding_period}_Periods'] = df['Close'].shift(-holding_period) / df['Close']\n", 139 | " \n", 140 | " fvg_subset = df[df['Bear_FVG_Val'] < 0]\n", 141 | " print(f'{len(fvg_subset)} Bearish FVGS')\n", 142 | " print(f'Bear FVG {holding_period} Period Average returns: {round((fvg_subset[f'Returns_In_{holding_period}_Periods'].mean() - 1) * 100, 2)}%')\n", 143 | "\n", 144 | " win_rate = (fvg_subset[f'Returns_In_{holding_period}_Periods'] > 1).mean() * 100\n", 145 | " print(f'Bear FVG Win Rate: {round(win_rate, 2)}%')\n", 146 | "\n", 147 | "\n", 148 | " # define quantiles\n", 149 | " lower = fvg_subset['Bear_FVG_Val'].quantile(.25)\n", 150 | " upper = fvg_subset['Bear_FVG_Val'].quantile(.75)\n", 151 | "\n", 152 | " # subset\n", 153 | " fvg_medium_val = fvg_subset[\n", 154 | " (fvg_subset['Bear_FVG_Val'] >= lower) & (fvg_subset['Bear_FVG_Val'] <= upper)\n", 155 | " ]\n", 156 | " print(f'{len(fvg_medium_val)} Medium Sized Bearish FVGs')\n", 157 | " print(f'Medium Bear FVG {holding_period} Period Average returns: {round((fvg_medium_val[f'Returns_In_{holding_period}_Periods'].mean() - 1) * 100, 2)}%')\n", 158 | "\n", 159 | " win_rate_medium = (fvg_medium_val[f'Returns_In_{holding_period}_Periods'] > 1).mean() * 100\n", 160 | " print(f'Medium Bull FVG Win Rate: {round(win_rate_medium, 2)}%')\n", 161 | "\n", 162 | " return df\n", 163 | "\n", 164 | "def main():\n", 165 | " df = get_data()\n", 166 | " df = bull_fvg(df)\n", 167 | " df = bear_fvg(df)\n", 168 | " df = assess_bull_FVG(df)\n", 169 | " df = assess_bear_FVG(df)\n", 170 | "\n", 171 | " return df\n", 172 | "\n", 173 | "df = main()" 174 | ] 175 | } 176 | ], 177 | "metadata": { 178 | "kernelspec": { 179 | "display_name": "myenv", 180 | "language": "python", 181 | "name": "python3" 182 | }, 183 | "language_info": { 184 | "codemirror_mode": { 185 | "name": "ipython", 186 | "version": 3 187 | }, 188 | "file_extension": ".py", 189 | "mimetype": "text/x-python", 190 | "name": "python", 191 | "nbconvert_exporter": "python", 192 | "pygments_lexer": "ipython3", 193 | "version": "3.13.1" 194 | } 195 | }, 196 | "nbformat": 4, 197 | "nbformat_minor": 5 198 | } 199 | -------------------------------------------------------------------------------- /StandardDev.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 35, 6 | "id": "97513ea7", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import yfinance as yf\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import pandas as pd\n", 13 | "import numpy as np" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 36, 19 | "id": "f456497e", 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stderr", 24 | "output_type": "stream", 25 | "text": [ 26 | "[*********************100%***********************] 1 of 1 completed\n" 27 | ] 28 | }, 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "0.04685378380107965\n", 34 | "1.177809619412867\n" 35 | ] 36 | }, 37 | { 38 | "data": { 39 | "image/png": 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", 40 | "text/plain": [ 41 | "
" 42 | ] 43 | }, 44 | "metadata": {}, 45 | "output_type": "display_data" 46 | }, 47 | { 48 | "data": { 49 | "image/png": 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", 50 | "text/plain": [ 51 | "
" 52 | ] 53 | }, 54 | "metadata": {}, 55 | "output_type": "display_data" 56 | } 57 | ], 58 | "source": [ 59 | "TICKER = 'SPY'\n", 60 | "INTERVAL = '1d'\n", 61 | "PERIOD = '730d' if INTERVAL == '1h' else 'max'\n", 62 | "\n", 63 | "LOOKBACK = 10000\n", 64 | "\n", 65 | "def get_data(ticker=TICKER, lookback=LOOKBACK, interval=INTERVAL):\n", 66 | " df = yf.download(ticker, interval=interval, auto_adjust=True, period=PERIOD)\n", 67 | " df.columns = df.columns.get_level_values(0)\n", 68 | " df = df.reset_index(drop=True)\n", 69 | "\n", 70 | " for c in df.columns:\n", 71 | " df[f'{c}_Change'] = df[c].pct_change() * 100\n", 72 | "\n", 73 | " # only return the subset of data you are interested in\n", 74 | " subset = df.iloc[-lookback:, :]\n", 75 | " plt.figure()\n", 76 | " plt.plot(subset['Close'])\n", 77 | " plt.title(f'Price Movements for {ticker} During Study')\n", 78 | "\n", 79 | " plt.figure()\n", 80 | " plt.hist(df['Close_Change'], bins=50)\n", 81 | "\n", 82 | " return subset.dropna()\n", 83 | "\n", 84 | "def add_big_price_movement(df):\n", 85 | "\n", 86 | " close_change_avg = df['Close_Change'].mean()\n", 87 | " close_change_std = df['Close_Change'].std()\n", 88 | " print(close_change_avg)\n", 89 | " print(close_change_std)\n", 90 | "\n", 91 | " df['Big_Movement'] = np.where(df['Close_Change'] > close_change_avg + (close_change_std * 2), 1, \n", 92 | " np.where(df['Close_Change'] < close_change_avg - (close_change_std * 2), -1, 0))\n", 93 | "\n", 94 | " return df\n", 95 | "\n", 96 | "def main():\n", 97 | " df = get_data()\n", 98 | " df = add_big_price_movement(df)\n", 99 | " return df\n", 100 | "\n", 101 | "df = main()" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 37, 107 | "id": "c1472828", 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "data": { 112 | "text/plain": [ 113 | "Big_Movement\n", 114 | " 0 7802\n", 115 | "-1 229\n", 116 | " 1 167\n", 117 | "Name: count, dtype: int64" 118 | ] 119 | }, 120 | "execution_count": 37, 121 | "metadata": {}, 122 | "output_type": "execute_result" 123 | } 124 | ], 125 | "source": [ 126 | "df['Big_Movement'].value_counts()" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 46, 132 | "id": "660d3d7a", 133 | "metadata": {}, 134 | "outputs": [ 135 | { 136 | "data": { 137 | "text/plain": [ 138 | "np.float64(-0.24760406515041083)" 139 | ] 140 | }, 141 | "execution_count": 46, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "df['Day_After'] = df['Big_Movement'].shift(1)\n", 148 | "large_pos_shifts = df.loc[df['Day_After'] == 1]\n", 149 | "large_pos_shifts['Close_Change'].mean()" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 45, 155 | "id": "13317991", 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "data": { 160 | "text/plain": [ 161 | "np.float64(0.4004379998631678)" 162 | ] 163 | }, 164 | "execution_count": 45, 165 | "metadata": {}, 166 | "output_type": "execute_result" 167 | } 168 | ], 169 | "source": [ 170 | "df['Day_After'] = df['Big_Movement'].shift(1)\n", 171 | "large_neg_shifts = df.loc[df['Day_After'] == -1]\n", 172 | "large_neg_shifts['Close_Change'].mean()" 173 | ] 174 | } 175 | ], 176 | "metadata": { 177 | "kernelspec": { 178 | "display_name": "myenv", 179 | "language": "python", 180 | "name": "python3" 181 | }, 182 | "language_info": { 183 | "codemirror_mode": { 184 | "name": "ipython", 185 | "version": 3 186 | }, 187 | "file_extension": ".py", 188 | "mimetype": "text/x-python", 189 | "name": "python", 190 | "nbconvert_exporter": "python", 191 | "pygments_lexer": "ipython3", 192 | "version": "3.13.1" 193 | } 194 | }, 195 | "nbformat": 4, 196 | "nbformat_minor": 5 197 | } 198 | -------------------------------------------------------------------------------- /Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "f169f98c", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd\n", 11 | "import numpy as np\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "import yfinance as yf\n", 14 | "import sklearn as sk" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "id": "098d7d75", 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "TICKER = 'SPY'\n", 25 | "INTERVAL='1d'\n", 26 | "\n", 27 | "# set period based on interval\n", 28 | "if INTERVAL == '1h':\n", 29 | " PERIOD = '730d'\n", 30 | "else:\n", 31 | " PERIOD = 'max'\n", 32 | "\n", 33 | "SHIFT = 5\n", 34 | "RSI_LENGTH = 14\n", 35 | "OVERBOUGHT = 70\n", 36 | "OVERSOLD = 30\n", 37 | "\n", 38 | "# what subsetion of that data are you interested in\n", 39 | "LOOKBACK = 100\n", 40 | "\n", 41 | "def get_data(ticker=TICKER, lookback=LOOKBACK, interval=INTERVAL):\n", 42 | "\n", 43 | " # get data at interval you want\n", 44 | " df = yf.download(ticker, interval=interval, period=PERIOD)\n", 45 | " df.columns = df.columns.get_level_values(0)\n", 46 | "\n", 47 | " # reset the index to make plots prettier\n", 48 | " df = df.reset_index(drop=True)\n", 49 | "\n", 50 | " # only return the subset of data you are interested in\n", 51 | " return df.iloc[-lookback:, :]\n", 52 | "\n", 53 | "# define the target variable (also called dependent variable, or y)\n", 54 | "def add_target(df, shift=SHIFT):\n", 55 | "\n", 56 | " # what is the close price SHIFT days from now?\n", 57 | " df[f'Close + {shift}'] = df['Close'].shift(-shift)\n", 58 | "\n", 59 | " # what is the change in close price SHIFT days from now?\n", 60 | " df['Target'] = df[f'Close + {shift}'] - df['Close']\n", 61 | "\n", 62 | " return df\n", 63 | "\n", 64 | "def add_RSI(df, length=RSI_LENGTH):\n", 65 | "\n", 66 | " price_change = df['Close'].diff()\n", 67 | " \n", 68 | " # separate gains/losses\n", 69 | " gain = price_change.where(price_change > 0, 0)\n", 70 | " loss = -price_change.where(price_change < 0, 0)\n", 71 | "\n", 72 | " # average gain vs loss\n", 73 | " avg_gain = gain.rolling(window=length).mean()\n", 74 | " avg_loss = loss.rolling(window=length).mean()\n", 75 | "\n", 76 | " # calculate rsi\n", 77 | " rs = avg_gain / avg_loss\n", 78 | " rsi = 100 - (100 / (1 + rs))\n", 79 | " df['RSI'] = rsi\n", 80 | "\n", 81 | " # plot the relative strength index\n", 82 | " plt.plot(df['RSI'])\n", 83 | " plt.axhline(OVERBOUGHT, color='red')\n", 84 | " plt.axhline(OVERSOLD, color='green')\n", 85 | "\n", 86 | " return df.dropna()\n", 87 | "\n", 88 | "def generate_regression_output(df, x='RSI', y='Target'):\n", 89 | "\n", 90 | " subset = df[[x, y]].dropna()\n", 91 | "\n", 92 | " # reshape for sklearn\n", 93 | " X = subset[[x]].values # 2d\n", 94 | " y = subset[y].values # 1d\n", 95 | "\n", 96 | " model = sk.linear_model.LinearRegression()\n", 97 | " model.fit(X, y)\n", 98 | "\n", 99 | " # use the regression model to \"predict\" the target variable\n", 100 | " y_pred = model.predict(X)\n", 101 | "\n", 102 | " # what is the relationship between features and target?\n", 103 | " r2 = sk.metrics.r2_score(y, y_pred)\n", 104 | "\n", 105 | " # coef, intercept, r2... mse later on\n", 106 | " print(f\"Coefficient: {model.coef}\")\n", 107 | " print(f\"Intercept: {model.intercept}\")\n", 108 | "\n", 109 | " return\n", 110 | "\n", 111 | "def main():\n", 112 | " df = get_data()\n", 113 | " df = add_target(df)\n", 114 | " df = add_RSI(df)\n", 115 | "\n", 116 | " return df" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 38, 122 | "id": "ad418473", 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "name": "stderr", 127 | "output_type": "stream", 128 | "text": [ 129 | "[*********************100%***********************] 1 of 1 completed\n" 130 | ] 131 | }, 132 | { 133 | "data": { 134 | "text/html": [ 135 | "
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PriceCloseHighLowOpenVolumeClose + 5TargetRSI
8125564.340027567.500000562.760010566.47998037603400594.20001229.85998587.201428
8126582.989990583.000000577.039978581.46997178993600594.84997611.85998588.125861
8127586.840027589.080017582.840027583.40997367947200592.8499766.00994987.380068
8128587.590027588.979980585.539978587.80999866283500582.859985-4.73004285.101285
8129590.460022590.969971585.099976585.55999871268100583.090027-7.36999584.819878
8130594.200012594.500000589.280029591.25000076052100579.109985-15.09002785.702537
8131594.849976595.539978588.099976588.09997668168500591.150024-3.69995185.006047
8132592.849976594.049988589.599976593.09002760614500587.729980-5.11999582.101566
8133582.859985592.580017581.820007588.44000295197700590.0499887.19000268.553189
8134583.090027586.619995581.409973582.65997370860400589.3900156.29998864.158160
\n", 276 | "
" 277 | ], 278 | "text/plain": [ 279 | "Price Close High Low Open Volume Close + 5 \\\n", 280 | "8125 564.340027 567.500000 562.760010 566.479980 37603400 594.200012 \n", 281 | "8126 582.989990 583.000000 577.039978 581.469971 78993600 594.849976 \n", 282 | "8127 586.840027 589.080017 582.840027 583.409973 67947200 592.849976 \n", 283 | "8128 587.590027 588.979980 585.539978 587.809998 66283500 582.859985 \n", 284 | "8129 590.460022 590.969971 585.099976 585.559998 71268100 583.090027 \n", 285 | "8130 594.200012 594.500000 589.280029 591.250000 76052100 579.109985 \n", 286 | "8131 594.849976 595.539978 588.099976 588.099976 68168500 591.150024 \n", 287 | "8132 592.849976 594.049988 589.599976 593.090027 60614500 587.729980 \n", 288 | "8133 582.859985 592.580017 581.820007 588.440002 95197700 590.049988 \n", 289 | "8134 583.090027 586.619995 581.409973 582.659973 70860400 589.390015 \n", 290 | "\n", 291 | "Price Target RSI \n", 292 | "8125 29.859985 87.201428 \n", 293 | "8126 11.859985 88.125861 \n", 294 | "8127 6.009949 87.380068 \n", 295 | "8128 -4.730042 85.101285 \n", 296 | "8129 -7.369995 84.819878 \n", 297 | "8130 -15.090027 85.702537 \n", 298 | "8131 -3.699951 85.006047 \n", 299 | "8132 -5.119995 82.101566 \n", 300 | "8133 7.190002 68.553189 \n", 301 | "8134 6.299988 64.158160 " 302 | ] 303 | }, 304 | "execution_count": 38, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | }, 308 | { 309 | "data": { 310 | "image/png": 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311 | "text/plain": [ 312 | "
" 313 | ] 314 | }, 315 | "metadata": {}, 316 | "output_type": "display_data" 317 | } 318 | ], 319 | "source": [ 320 | "df = main()\n", 321 | "df.tail(10)" 322 | ] 323 | } 324 | ], 325 | "metadata": { 326 | "kernelspec": { 327 | "display_name": "myenv", 328 | "language": "python", 329 | "name": "python3" 330 | }, 331 | "language_info": { 332 | "codemirror_mode": { 333 | "name": "ipython", 334 | "version": 3 335 | }, 336 | "file_extension": ".py", 337 | "mimetype": "text/x-python", 338 | "name": "python", 339 | "nbconvert_exporter": "python", 340 | "pygments_lexer": "ipython3", 341 | "version": "3.13.1" 342 | } 343 | }, 344 | "nbformat": 4, 345 | "nbformat_minor": 5 346 | } 347 | -------------------------------------------------------------------------------- /MA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "id": "3c76f6e6", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import yfinance as yf\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import pandas as pd\n", 13 | "import numpy as np" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "id": "cd616896", 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stderr", 24 | "output_type": "stream", 25 | "text": [ 26 | "[*********************100%***********************] 1 of 1 completed\n" 27 | ] 28 | }, 29 | { 30 | "data": { 31 | "text/html": [ 32 | "
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PriceCloseHighLowOpenVolumeMA
Date
1993-11-1126.27744526.41910226.25973826.3482748890025.383038
1993-11-1226.40138426.48992026.31284926.33055610820025.392782
1993-11-1526.38367326.45450126.31284426.45450124330025.401569
1993-11-1626.50763526.52534226.33056326.43680649260025.410714
1993-11-1726.36596726.52533126.29513826.5253313960025.417846
.....................
2025-04-21513.880005521.700012508.459991521.15997369368100570.276754
2025-04-22527.250000529.299988519.190002520.14001575948100570.181573
2025-04-23535.419983545.429993533.880005540.42999390590700570.111491
2025-04-24546.690002547.429993535.450012536.71997164150400570.094589
2025-04-25550.640015551.049988543.690002546.65002461025700570.094763
\n", 169 | "

7917 rows × 6 columns

\n", 170 | "
" 171 | ], 172 | "text/plain": [ 173 | "Price Close High Low Open Volume \\\n", 174 | "Date \n", 175 | "1993-11-11 26.277445 26.419102 26.259738 26.348274 88900 \n", 176 | "1993-11-12 26.401384 26.489920 26.312849 26.330556 108200 \n", 177 | "1993-11-15 26.383673 26.454501 26.312844 26.454501 243300 \n", 178 | "1993-11-16 26.507635 26.525342 26.330563 26.436806 492600 \n", 179 | "1993-11-17 26.365967 26.525331 26.295138 26.525331 39600 \n", 180 | "... ... ... ... ... ... \n", 181 | "2025-04-21 513.880005 521.700012 508.459991 521.159973 69368100 \n", 182 | "2025-04-22 527.250000 529.299988 519.190002 520.140015 75948100 \n", 183 | "2025-04-23 535.419983 545.429993 533.880005 540.429993 90590700 \n", 184 | "2025-04-24 546.690002 547.429993 535.450012 536.719971 64150400 \n", 185 | "2025-04-25 550.640015 551.049988 543.690002 546.650024 61025700 \n", 186 | "\n", 187 | "Price MA \n", 188 | "Date \n", 189 | "1993-11-11 25.383038 \n", 190 | "1993-11-12 25.392782 \n", 191 | "1993-11-15 25.401569 \n", 192 | "1993-11-16 25.410714 \n", 193 | "1993-11-17 25.417846 \n", 194 | "... ... \n", 195 | "2025-04-21 570.276754 \n", 196 | "2025-04-22 570.181573 \n", 197 | "2025-04-23 570.111491 \n", 198 | "2025-04-24 570.094589 \n", 199 | "2025-04-25 570.094763 \n", 200 | "\n", 201 | "[7917 rows x 6 columns]" 202 | ] 203 | }, 204 | "execution_count": 6, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | }, 208 | { 209 | "data": { 210 | "image/png": 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" 213 | ] 214 | }, 215 | "metadata": {}, 216 | "output_type": "display_data" 217 | } 218 | ], 219 | "source": [ 220 | "TICKER = 'SPY'\n", 221 | "WINDOW = 200\n", 222 | "\n", 223 | "def get_data():\n", 224 | " df = yf.download(TICKER)\n", 225 | " df.columns = df.columns.get_level_values(0)\n", 226 | "\n", 227 | " # add the moving average column\n", 228 | " df['MA'] = df['Close'].rolling(WINDOW).mean()\n", 229 | "\n", 230 | " # plot the information\n", 231 | " plt.plot(df['Close'])\n", 232 | " plt.plot(df['MA'])\n", 233 | " plt.title(f'{TICKER} Close Price With {WINDOW} Moving Average')\n", 234 | "\n", 235 | " return df.dropna()\n", 236 | "\n", 237 | "def main():\n", 238 | " df = get_data()\n", 239 | " return df\n", 240 | "\n", 241 | "main()" 242 | ] 243 | } 244 | ], 245 | "metadata": { 246 | "kernelspec": { 247 | "display_name": "myenv", 248 | "language": "python", 249 | "name": "python3" 250 | }, 251 | "language_info": { 252 | "codemirror_mode": { 253 | "name": "ipython", 254 | "version": 3 255 | }, 256 | "file_extension": ".py", 257 | "mimetype": "text/x-python", 258 | "name": "python", 259 | "nbconvert_exporter": "python", 260 | "pygments_lexer": "ipython3", 261 | "version": "3.13.1" 262 | } 263 | }, 264 | "nbformat": 4, 265 | "nbformat_minor": 5 266 | } 267 | --------------------------------------------------------------------------------