From b13cd022dcdcf617343b53f29c058297ed291d93 Mon Sep 17 00:00:00 2001 From: Luis Barrancos Date: Fri, 13 Oct 2023 08:22:29 +0100 Subject: [PATCH] Add True Momentum Oscillator (TMO) Added TMO indicator, defaults 14/5/3 values. See documentation for the calculation, defaults, and variants presented by some common platforms. Added example jupyterlab notebook showing how with the present information is trivial to compute the variants that other platforms present. Added plots with matplotlib, plotly, bokeh, with conditional shading. --- examples/TMO_indicator.ipynb | 4018 ++++++++++++++++++++++++++++++ pandas_ta/core.py | 6 + pandas_ta/maps.py | 2 +- pandas_ta/momentum/__init__.py | 2 + pandas_ta/momentum/tmo.py | 131 + tests/test_indicator_momentum.py | 10 + 6 files changed, 4168 insertions(+), 1 deletion(-) create mode 100644 examples/TMO_indicator.ipynb create mode 100644 pandas_ta/momentum/tmo.py diff --git a/examples/TMO_indicator.ipynb b/examples/TMO_indicator.ipynb new file mode 100644 index 00000000..453ccf31 --- /dev/null +++ b/examples/TMO_indicator.ipynb @@ -0,0 +1,4018 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1bbca82a-f1fc-4a57-8e0b-e50c72317207", + "metadata": {}, + "source": [ + "# Example of Pandas-TA technical indicators with multiple plot backends\n", + "\n", + "The plotting backends will be matplotlib, plotly, and bokeh mostly. Matplotlib is flexible, but not dynamic, and the others are dynamic, but don\"t have all the features that matplotlib has. The most problematic for technical analysis are related to the need to shade segments of indicator lines differently according to some criteria, or to shade areas between lines, shade them differently, or shading histogram bars differently. All of these present some issues, and we show below how to use Pandas-TA and some backends to achieve the functionality you would expect from some indicators." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8b0092aa-188f-42c9-a2cd-0988b2600304", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[*********************100%***********************] 1 of 1 completed\n", + "\n", + "DatetimeIndex: 155 entries, 2023-09-13 09:30:00-04:00 to 2023-10-12 16:00:00-04:00\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Open 155 non-null float64\n", + " 1 High 155 non-null float64\n", + " 2 Low 155 non-null float64\n", + " 3 Close 155 non-null float64\n", + " 4 Adj Close 155 non-null float64\n", + " 5 Volume 155 non-null int64 \n", + "dtypes: float64(5), int64(1)\n", + "memory usage: 8.5 KB\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pandas_ta as ta\n", + "\n", + "import yfinance as yf\n", + "symbol = \"XOM\"\n", + "tickerdata = yf.download(symbol, period=\"1mo\", interval=\"1h\")\n", + "tickerdata.info()" + ] + }, + { + "cell_type": "markdown", + "id": "2ff8e437-bd50-4367-b33d-2fd08cb9c494", + "metadata": {}, + "source": [ + "## TMO: True Momentum Oscillator\n", + "\n", + "This indicator computes the signum of the price difference of an asset for a duration of *tmo_length* and then smoothes it with a moving average with a window *calc_length*. This smoothed value is then smoothed again with another moving average with window *smooth_length* to generate the TMO **main signal**, and this in turn is smoothed again with another moving average with the same *smooth_length* window to generate the TMO\"s **smooth signal**.\n", + "Some indicators normalize the result so that it lies within the $[-100,100]$ range, which allows for easy setting of overbought and oversold regions at 70 and -70 respectively, or 80 and -80.\n", + "\n", + "The default values for the moving averages and the summation are $\\text{TMO}_{length}=14, \\ \\text{Calc}_{length}=5, \\ \\text{Smooth}_{length}=3$ The occasional [exceptions](https://forexobroker.com/true-momentum-oscillator-indicator/) in [some platforms](https://www.tradingview.com/script/HibAeQPA-TMO-with-TTM-Squeeze/) might set $\\text{Smooth}_{length}=6$ or $\\text{TMO}_{length}=21$, but the majority uses the $14/5/3$ values and a default exponential moving average.\n", + "Computing an histogram is trivial from the provided fields in the dataframe.\n", + "Some other indicators go further, by computing the momentum of the TMO\"s **main signal** and **smooth_signal**.\n", + "\n", + "Both the normalization and the momentum computation are **not** done by default." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9edb6879-14d0-4dad-9152-e8468654a87d", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use(\"dark_background\")\n", + "\n", + "from pandas_ta.momentum import tmo" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0cea5ac3-f8aa-4476-a0ab-c345e98d5552", + "metadata": {}, + "outputs": [], + "source": [ + "# True Momentum Oscillator calculation\n", + "tmo_length = 14\n", + "calc_length = 5\n", + "smooth_length = 3\n", + "compute_momentum = True\n", + "normalize_signal = False\n", + "\n", + "data = tmo(\n", + " tickerdata[\"Open\"], tickerdata[\"Close\"], \n", + " tmo_length=tmo_length, calc_length=calc_length, smooth_length=smooth_length, \n", + " mamode=\"ema\", compute_momentum=compute_momentum, normalize_signal=normalize_signal)\n", + "\n", + "data.dropna()\n", + "data = data.dropna().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e56c729e-679c-4740-a9ac-a5d999919e05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 120 entries, 0 to 119\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 index 120 non-null datetime64[ns, America/New_York]\n", + " 1 TMO_14_5_3 120 non-null float64 \n", + " 2 TMO_Smooth_14_5_3 120 non-null float64 \n", + " 3 TMO_Main_Mom_14_5_3 120 non-null float64 \n", + " 4 TMO_Smooth_Mom_14_5_3 120 non-null float64 \n", + "dtypes: datetime64[ns, America/New_York](1), float64(4)\n", + "memory usage: 4.8 KB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "id": "bfb52f7a-1185-41ea-bcc2-ce718cea3669", + "metadata": {}, + "source": [ + "### TMO: Matplotlib\n", + "\n", + "We compute the unnormalized TMO and the optional momentums, with the default EMA moving average and default periods.\n", + "Similarly to this [TMO version](https://www.tradingview.com/script/VRwDppqd-True-Momentum-Oscillator/), the information provided in the dataframe suffices to shade the TMO main signal green if above the smoth signal, or red if below. The additional histogram can be computed trivially from the returned values of the TMO dataframe. This histogram reflects the difference between the TMO main signal and the TMO smooth signal, and is shaded green or red, if positive or negative.\n", + "Note that since we shade areas between a non traced TMO value and its smooth value, we need to add a custom legend and label. That\"s the purpose of ```HandlerTuple```. See below.\n", + "Values are not normalized." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68242994-46dd-4ab8-8d81-84c4bac613c4", + "metadata": {}, + "outputs": [], + "source": [ + "# to deal with shaded traces\n", + "from matplotlib.legend_handler import HandlerTuple\n", + "from scipy.interpolate import make_interp_spline\n", + "\n", + "# interpolation density for trace spline interpolation\n", + "# if you want the linear data as is without interpolation, use interpolate=False\n", + "density = 500\n", + "interpolate=False\n", + "\n", + "def compute_interp_data(x, y):\n", + " xy_spline = make_interp_spline(x, y)\n", + " x_ = np.linspace(x.min(), x.max(), density)\n", + " y_ = xy_spline(x_)\n", + " return (x_, y_)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ef39cf71-f996-48ee-8d65-866115e32fa7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(18, 10))\n", + "\n", + "# Plot momentum lines, but the dataframe can contain any parameter names combination\n", + "# so access by index position as seen above in the data.info() \n", + "tmo_main = data.iloc[:, 1]\n", + "tmo_smooth = data.iloc[:, 2]\n", + "\n", + "if compute_momentum is True:\n", + " tmo_main_mom = data.iloc[:, 3]\n", + " tmo_smooth_mom = data.iloc[:, 4]\n", + " if interpolate is False:\n", + " ax.plot(data.index, tmo_main_mom, color=\"orange\", label=data.columns[3])\n", + " ax.plot(data.index, tmo_smooth_mom, color=\"yellow\", label=data.columns[4])\n", + " else:\n", + " x, y = compute_interp_data(data.index, tmo_main_mom)\n", + " ax.plot(x, y, color=\"orange\", label=data.columns[3])\n", + " x, y = compute_interp_data(data.index, tmo_smooth_mom)\n", + " ax.plot(x, y, color=\"yellow\", label=data.columns[4]) \n", + "\n", + "# Main TMO line (for reference, we are shading only the areas betweem main and smooth signal)\n", + "# ax.plot(data.index, tmo_main, color=\"grey\", alpha=0.7, linestyle=\":\", label=data.columns[1])\n", + "\n", + "# Plot Smooth Signal\n", + "if interpolate is False:\n", + " ax.plot(data.index, tmo_smooth, color=\"grey\", label=data.columns[2])\n", + "else:\n", + " x, y = compute_interp_data(data.index, tmo_smooth)\n", + " ax.plot(x, y, color=\"grey\", label=data.columns[2])\n", + "\n", + "# Conditional fill between Main Signal and Smooth Signal\n", + "if interpolate is False:\n", + " ax.fill_between(data.index, tmo_main, tmo_smooth, \n", + " where=(tmo_main > tmo_smooth), interpolate=True, color=\"green\", alpha=0.5,\n", + " label=data.columns[1])\n", + " ax.fill_between(data.index, tmo_main, tmo_smooth, \n", + " where=(tmo_main <= tmo_smooth), interpolate=True, color=\"red\", alpha=0.5,\n", + " label=data.columns[1])\n", + "else:\n", + " x1, y1 = compute_interp_data(data.index, tmo_main)\n", + " x2, y2 = compute_interp_data(data.index, tmo_smooth)\n", + " ax.fill_between(x1, y1, y2, \n", + " where=(y1 > y2), interpolate=True, color=\"green\", alpha=0.5,\n", + " label=data.columns[1])\n", + " ax.fill_between(x1, y1, y2, \n", + " where=(y1 <= y2), interpolate=True, color=\"red\", alpha=0.5,\n", + " label=data.columns[1]) \n", + "\n", + "# Adding histogram\n", + "if interpolate is False:\n", + " colors = [\"g\" if main > smooth else \"r\" for main, smooth in zip(tmo_main, tmo_smooth)]\n", + "else:\n", + " x1, y1 = compute_interp_data(data.index, tmo_main)\n", + " x2, y2 = compute_interp_data(data.index, tmo_smooth)\n", + " colors = [\"g\" if main > smooth else \"r\" for main, smooth in zip(y1, y2)]\n", + " \n", + "ax.bar(data.index, tmo_main - tmo_smooth, color=colors, alpha=0.3)\n", + "\n", + "# Get handles and labels\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "unique_labels = list(np.unique(labels))\n", + "\n", + "# Combine the handlers using HandlerTuple\n", + "combined_handles = [tuple([h for h, l in zip(handles, labels) if l == label]) for label in unique_labels]\n", + "\n", + "# Create the legend with combined handles and unique labels\n", + "ax.legend(handles=combined_handles, labels=unique_labels + [\"\"], handlelength=3,\n", + " handler_map={tuple: HandlerTuple(ndivide=None, pad=0)})\n", + "\n", + "#plt.legend()\n", + "plt.title(f\"{str(symbol)[0:]} TMO and momentum, non normalized\", fontsize=14, color=\"white\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "060cdf72-f953-4680-9b10-5ccad23e1072", + "metadata": {}, + "source": [ + "Next we check if the TMO indicator can be accessed directly from within the Pandas dataframe, besides as a regular indicator, and dump the information on the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "438c605d-fbf9-4010-ad07-3f404fc2d76c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(18, 10))\n", + "\n", + "compute_momentum = False\n", + "normalize_signal = True\n", + "\n", + "data = tmo(\n", + " tickerdata[\"Open\"], tickerdata[\"Close\"], \n", + " tmo_length=tmo_length, calc_length=calc_length, smooth_length=smooth_length, \n", + " mamode=\"ema\", compute_momentum=compute_momentum, normalize_signal=normalize_signal)\n", + "\n", + "data.dropna()\n", + "data = data.dropna().reset_index()\n", + "tmo_main = data.iloc[:, 1]\n", + "tmo_smooth = data.iloc[:, 2]\n", + "\n", + "if compute_momentum is True:\n", + " tmo_main_mom = data.iloc[:, 3]\n", + " tmo_smooth_mom = data.iloc[:, 4]\n", + " if interpolate is False:\n", + " ax.plot(data.index, tmo_main_mom, color=\"orange\", label=data.columns[3])\n", + " ax.plot(data.index, tmo_smooth_mom, color=\"yellow\", label=data.columns[4])\n", + " else:\n", + " x, y = compute_interp_data(data.index, tmo_main_mom)\n", + " ax.plot(x, y, color=\"orange\", label=data.columns[3])\n", + " x, y = compute_interp_data(data.index, tmo_smooth_mom)\n", + " ax.plot(x, y, color=\"yellow\", label=data.columns[4]) \n", + "\n", + "# Main TMO line (for reference, we are shading only the areas betweem main and smooth signal)\n", + "# ax.plot(data.index, tmo_main, color=\"grey\", alpha=0.7, linestyle=\":\", label=data.columns[1])\n", + "\n", + "# Plot Smooth Signal\n", + "if interpolate is False:\n", + " ax.plot(data.index, tmo_smooth, color=\"grey\", label=data.columns[2])\n", + "else:\n", + " x, y = compute_interp_data(data.index, tmo_smooth)\n", + " ax.plot(x, y, color=\"grey\", label=data.columns[2])\n", + "\n", + "# Conditional fill between Main Signal and Smooth Signal\n", + "if interpolate is False:\n", + " ax.fill_between(data.index, tmo_main, tmo_smooth, \n", + " where=(tmo_main > tmo_smooth), interpolate=True, color=\"green\", alpha=0.5,\n", + " label=data.columns[1])\n", + " ax.fill_between(data.index, tmo_main, tmo_smooth, \n", + " where=(tmo_main <= tmo_smooth), interpolate=True, color=\"red\", alpha=0.5,\n", + " label=data.columns[1])\n", + "else:\n", + " x1, y1 = compute_interp_data(data.index, tmo_main)\n", + " x2, y2 = compute_interp_data(data.index, tmo_smooth)\n", + " ax.fill_between(x1, y1, y2, \n", + " where=(y1 > y2), interpolate=True, color=\"green\", alpha=0.5,\n", + " label=data.columns[1])\n", + " ax.fill_between(x1, y1, y2, \n", + " where=(y1 <= y2), interpolate=True, color=\"red\", alpha=0.5,\n", + " label=data.columns[1]) \n", + "\n", + "# Adding histogram\n", + "if interpolate is False:\n", + " colors = [\"g\" if main > smooth else \"r\" for main, smooth in zip(tmo_main, tmo_smooth)]\n", + "else:\n", + " x1, y1 = compute_interp_data(data.index, tmo_main)\n", + " x2, y2 = compute_interp_data(data.index, tmo_smooth)\n", + " colors = [\"g\" if main > smooth else \"r\" for main, smooth in zip(y1, y2)]\n", + " \n", + "ax.bar(data.index, tmo_main - tmo_smooth, color=colors, alpha=0.3)\n", + "\n", + "# Get handles and labels\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "unique_labels = list(np.unique(labels))\n", + "\n", + "# Combine the handlers using HandlerTuple\n", + "combined_handles = [tuple([h for h, l in zip(handles, labels) if l == label]) for label in unique_labels]\n", + "\n", + "# Create the legend with combined handles and unique labels\n", + "ax.legend(handles=combined_handles, labels=unique_labels + [\"\"], handlelength=3,\n", + " handler_map={tuple: HandlerTuple(ndivide=None, pad=0)})\n", + "\n", + "plt.title(f\"{str(symbol)[0:]} TMO, normalized\", fontsize=14, color=\"white\")\n", + "#plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5c55efc8-c54d-4ca8-9455-156f9f609c5d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "DatetimeIndex: 155 entries, 2023-09-13 09:30:00-04:00 to 2023-10-12 16:00:00-04:00\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Open 155 non-null float64\n", + " 1 High 155 non-null float64\n", + " 2 Low 155 non-null float64\n", + " 3 Close 155 non-null float64\n", + " 4 Adj Close 155 non-null float64\n", + " 5 Volume 155 non-null int64 \n", + "dtypes: float64(5), int64(1)\n", + "memory usage: 8.5 KB\n" + ] + } + ], + "source": [ + "tickerdata.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36fa4e28-64d7-4ab0-90d2-f6ef8fe7f11b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TMO_14_5_3TMO_Smooth_14_5_3TMO_Main_Mom_14_5_3TMO_Smooth_Mom_14_5_3
2023-09-13 09:30:00-04:00NaNNaN00
2023-09-13 10:30:00-04:00NaNNaN00
2023-09-13 11:30:00-04:00NaNNaN00
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2023-09-13 13:30:00-04:00NaNNaN00
...............
2023-10-12 12:30:00-04:00-3.538480-3.33828000
2023-10-12 13:30:00-04:00-4.120195-3.72923700
2023-10-12 14:30:00-04:00-4.294068-4.01165300
2023-10-12 15:30:00-04:00-4.303014-4.15733300
2023-10-12 16:00:00-04:00-4.088827-4.12308000
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155 rows × 4 columns

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" + ], + "text/plain": [ + " TMO_14_5_3 TMO_Smooth_14_5_3 TMO_Main_Mom_14_5_3 \\\n", + "2023-09-13 09:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 10:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 11:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 12:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 13:30:00-04:00 NaN NaN 0 \n", + "... ... ... ... \n", + "2023-10-12 12:30:00-04:00 -3.538480 -3.338280 0 \n", + "2023-10-12 13:30:00-04:00 -4.120195 -3.729237 0 \n", + "2023-10-12 14:30:00-04:00 -4.294068 -4.011653 0 \n", + "2023-10-12 15:30:00-04:00 -4.303014 -4.157333 0 \n", + "2023-10-12 16:00:00-04:00 -4.088827 -4.123080 0 \n", + "\n", + " TMO_Smooth_Mom_14_5_3 \n", + "2023-09-13 09:30:00-04:00 0 \n", + "2023-09-13 10:30:00-04:00 0 \n", + "2023-09-13 11:30:00-04:00 0 \n", + "2023-09-13 12:30:00-04:00 0 \n", + "2023-09-13 13:30:00-04:00 0 \n", + "... ... \n", + "2023-10-12 12:30:00-04:00 0 \n", + "2023-10-12 13:30:00-04:00 0 \n", + "2023-10-12 14:30:00-04:00 0 \n", + "2023-10-12 15:30:00-04:00 0 \n", + "2023-10-12 16:00:00-04:00 0 \n", + "\n", + "[155 rows x 4 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# direct invocation\n", + "tmo(tickerdata[\"Open\"], tickerdata[\"Close\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "646453a7-67ca-42d7-b95e-95c4710e0fb9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TMO_14_5_3TMO_Smooth_14_5_3TMO_Main_Mom_14_5_3TMO_Smooth_Mom_14_5_3
2023-09-13 09:30:00-04:00NaNNaN00
2023-09-13 10:30:00-04:00NaNNaN00
2023-09-13 11:30:00-04:00NaNNaN00
2023-09-13 12:30:00-04:00NaNNaN00
2023-09-13 13:30:00-04:00NaNNaN00
...............
2023-10-12 12:30:00-04:00-3.538480-3.33828000
2023-10-12 13:30:00-04:00-4.120195-3.72923700
2023-10-12 14:30:00-04:00-4.294068-4.01165300
2023-10-12 15:30:00-04:00-4.303014-4.15733300
2023-10-12 16:00:00-04:00-4.088827-4.12308000
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155 rows × 4 columns

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" + ], + "text/plain": [ + " TMO_14_5_3 TMO_Smooth_14_5_3 TMO_Main_Mom_14_5_3 \\\n", + "2023-09-13 09:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 10:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 11:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 12:30:00-04:00 NaN NaN 0 \n", + "2023-09-13 13:30:00-04:00 NaN NaN 0 \n", + "... ... ... ... \n", + "2023-10-12 12:30:00-04:00 -3.538480 -3.338280 0 \n", + "2023-10-12 13:30:00-04:00 -4.120195 -3.729237 0 \n", + "2023-10-12 14:30:00-04:00 -4.294068 -4.011653 0 \n", + "2023-10-12 15:30:00-04:00 -4.303014 -4.157333 0 \n", + "2023-10-12 16:00:00-04:00 -4.088827 -4.123080 0 \n", + "\n", + " TMO_Smooth_Mom_14_5_3 \n", + "2023-09-13 09:30:00-04:00 0 \n", + "2023-09-13 10:30:00-04:00 0 \n", + "2023-09-13 11:30:00-04:00 0 \n", + "2023-09-13 12:30:00-04:00 0 \n", + "2023-09-13 13:30:00-04:00 0 \n", + "... ... \n", + "2023-10-12 12:30:00-04:00 0 \n", + "2023-10-12 13:30:00-04:00 0 \n", + "2023-10-12 14:30:00-04:00 0 \n", + "2023-10-12 15:30:00-04:00 0 \n", + "2023-10-12 16:00:00-04:00 0 \n", + "\n", + "[155 rows x 4 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TMO called from the dataframe\"s Pandas-TA extension\n", + "df = tickerdata.copy()\n", + "\n", + "df.ta.tmo(append=True)" + ] + }, + { + "cell_type": "markdown", + "id": "35cd3890-6f2e-4afb-a9e4-c39d8fabb667", + "metadata": {}, + "source": [ + "### TMO: Plotly\n", + "\n", + "With Plotly, to the best of my knowledge, we can't shade the areas between the lines. Not without artifacts at least. But we can shade the TMO **main signal** line red or green, according to it being below or above the TMO **smooth signal** line. Sadly this seems to result in a plethora of traces, which get their legend entries, rather than a single trace with a legend we could add and shade separately as we did with matplotlib.\n", + "Overbought and oversold areas are added, and for that we pass ```normalize_signal=True``` to the TMO indicator." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5512a2f9-571c-4cf6-8b1f-2defb9ba5969", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "import plotly.graph_objects as go\n", + "\n", + "compute_momentum=True\n", + "normalize_signal=True\n", + "\n", + "data = tmo(\n", + " tickerdata[\"Open\"], tickerdata[\"Close\"], \n", + " tmo_length=tmo_length, calc_length=calc_length, smooth_length=smooth_length, \n", + " mamode=\"ema\", compute_momentum=compute_momentum, normalize_signal=True)\n", + "\n", + "data.dropna()\n", + "data = data.dropna().reset_index()\n", + "\n", + "tmo_main = data.iloc[:, 1]\n", + "tmo_smooth = data.iloc[:, 2]\n", + "\n", + "if compute_momentum is True:\n", + " tmo_main_mom = data.iloc[:, 3]\n", + " tmo_smooth_mom = data.iloc[:, 4]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a3fe5c53-ee83-4c5f-9d3c-b295baf22986", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "orange", + "width": 1 + }, + "mode": "lines", + "name": "TMO_Main_Mom_14_5_3", + "type": "scatter", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, 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+ "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "# somehow we have extra spacing for non-trading days, fix this with datetime and tick spacing\n", + "tick_spacing = len(data.index) // 10\n", + "tickvals = data.index[::tick_spacing]\n", + "ticktext = [datetime.strftime(pd.Timestamp(date), \"%d/%m/%y\") for date in tickvals]\n", + "\n", + "fig.add_trace(go.Scatter(x=data.index, y=tmo_main_mom, mode=\"lines\", name=data.columns[3], line=dict(color=\"orange\", width=1.0)))\n", + "fig.add_trace(go.Scatter(x=data.index, y=tmo_smooth_mom, mode=\"lines\", name=data.columns[4], line=dict(color=\"yellow\", width=1.0)))\n", + "\n", + "#fig.add_trace(go.Scatter(x=data.index, y=tmo_main, mode=\"lines\", name=data.columns[1], line=dict(color=\"red\")))\n", + "fig.add_trace(go.Scatter(x=data.index, y=tmo_smooth, mode=\"lines\", name=data.columns[2], line=dict(color=\"grey\", width=1.0, dash=\"dot\")))\n", + "\n", + "# The default wasn't very readable, so adjust the scaling of the histogram bars (height)\n", + "# and the opacity as needed.\n", + "scaling_factor = 2.0\n", + "bar_opacity = 0.3\n", + "\n", + "# Add the histogram, and scale by a completely arbitrary user factor\n", + "colors = [\"green\" if main > smooth else \"red\" for main, smooth in zip(tmo_main, tmo_smooth)]\n", + "scaled_histogram_values = (tmo_main - tmo_smooth) * scaling_factor\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=scaled_histogram_values, marker=dict(color=colors, opacity=bar_opacity), name=\"Main-Smooth Histogram\"))\n", + "\n", + "# Create MainSignal line as segments to allow multiple colors\n", + "last_switch_point = 0 \n", + "current_color = colors[0]\n", + "\n", + "# Create a custom legend for TMO main signal with legendgroup, to keep the\n", + "# red and green legend associated with the main signal. This is the best we can do for now.\n", + "for i in range(1, len(colors)):\n", + " if colors[i] != current_color:\n", + " fig.add_trace(go.Scatter(x=data.index[last_switch_point:i+1], y=data[data.columns[1]].iloc[last_switch_point:i+1],\n", + " mode=\"lines\", name=data.columns[1], showlegend=False, line=dict(color=current_color, width=2.0),\n", + " legendgroup=\"TMO Main Signal\"))\n", + " last_switch_point = i\n", + " current_color = colors[i]\n", + "\n", + "# and add the last segment\n", + "fig.add_trace(go.Scatter(x=data.index[last_switch_point:], y=data[data.columns[1]].iloc[last_switch_point:],\n", + " mode=\"lines\", name=data.columns[1], showlegend=False, line=dict(color=current_color, width=2.0),\n", + " legendgroup=\"TMO Main Signal\"))\n", + "\n", + "fig.add_trace(go.Scatter(x=[None], y=[None], mode=\"lines\", name=\"TMO Main Signal\",\n", + " legendgroup=\"TMO Main Signal\", line=dict(color=\"red\", width=2.0)))\n", + "fig.add_trace(go.Scatter(x=[None], y=[None], mode=\"lines\", name=\"TMO Main Signal\",\n", + " legendgroup=\"TMO Main Signal\", line=dict(color=\"green\", width=2.0)))\n", + "\n", + "# Add overbought and oversold lines at y=70 and y=-70 respectively\n", + "# Add horizontal dashed lines at y=70 and y=-70\n", + "fig.add_shape(\n", + " go.layout.Shape(type=\"line\", x0=data.index.min(), x1=data.index.max(), y0=70, y1=70, line=dict(color=\"red\", dash=\"dash\", width=1.0), opacity=0.3,\n", + " ))\n", + "fig.add_shape(\n", + " go.layout.Shape(type=\"line\", x0=data.index.min(), x1=data.index.max(), y0=-70, y1=-70, line=dict(color=\"green\", dash=\"dash\", width=1.0), opacity=0.3,\n", + " ))\n", + "fig.add_shape(\n", + " go.layout.Shape(type=\"rect\", x0=data.index.min(), x1=data.index.max(), y0=70, y1=100, fillcolor=\"red\", opacity=0.05, layer=\"below\",\n", + " ))\n", + "fig.add_shape(\n", + " go.layout.Shape(type=\"rect\", x0=data.index.min(), x1=data.index.max(), y0=-100, y1=-70, fillcolor=\"green\", opacity=0.05, layer=\"below\",\n", + " ))\n", + "\n", + "fig.update_layout(\n", + " title=f\"{str(symbol)[0:]} stock with TMO indicator\",\n", + " xaxis=dict(\n", + " title=\"Date\",\n", + " tickvals=tickvals,\n", + " ticktext=ticktext,\n", + " type=\"category\"\n", + " ),\n", + " yaxis=dict(title=\"Value\"),\n", + " yaxis2=dict(title=\"Difference\", overlaying=\"y\", side=\"right\"),\n", + " height=600,\n", + " template=\"plotly_dark\"\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "91523275-112e-4af3-935f-e408b9e74c34", + "metadata": {}, + "source": [ + "### TMO: Bokeh\n", + "\n", + "As yet another alternative bokeh is provided. It suffers from the same fallbacks as plotly in regard to dynamic shading, so we split segments into N traces and shade them accordingly. A custom legend is needed. \n", + "If you want to create advanced plots with conditional traces, you either use matplotlib, or use something more dynamic but limited." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "42051c34-52ae-478b-9363-bb8612a3a6c4", + "metadata": {}, + "outputs": [], + "source": [ + "from bokeh.io import output_notebook, curdoc\n", + "from bokeh.models import ColumnDataSource, LegendItem\n", + "from bokeh.plotting import figure, show\n", + "\n", + "curdoc().theme = \"dark_minimal\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6adc347a-27a6-4e05-a231-af7d5e01633e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"cbc25571-a243-4a8e-b3c5-16b886771efd\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"cbc25571-a243-4a8e-b3c5-16b886771efd\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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+ " const render_items = [{\"docid\":\"7b6dc393-dda4-4908-87d3-c063ab2f6f8c\",\"roots\":{\"p1001\":\"b97f04ce-df41-45da-96dd-7ee72e20af6c\"},\"root_ids\":[\"p1001\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1001" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(title=f\"{str(symbol)[0:]} TMO and momentum, normalized\", x_axis_type=\"datetime\", width=1200, height=500)\n", + "\n", + "green_data = data[tmo_main > tmo_smooth]\n", + "red_data = data[tmo_main <= tmo_smooth]\n", + "\n", + "# We need to use the same approach as plotly, since bokeh cannot shade the line trace\n", + "# dynamically. Instead, break the line in N segments.\n", + "xs, ys, colors = [], [], []\n", + "\n", + "for i in range(1, len(tmo_main)):\n", + " xs.append([tmo_main.index[i-1], tmo_main.index[i]])\n", + " ys.append([tmo_main.iloc[i-1], tmo_main.iloc[i]])\n", + " color = \"green\" if tmo_main.iloc[i] > tmo_smooth.iloc[i] else \"red\"\n", + " colors.append(color)\n", + "\n", + "# so shade the N individual segments\n", + "for i in range(len(xs)):\n", + " p.line(xs[i], ys[i], line_width=2, line_color=colors[i])\n", + "\n", + "#p.line(data.index, tmo_main, color=\"red\", legend_label=data.columns[1])\n", + "p.line(data.index, tmo_smooth, color=\"grey\", legend_label=data.columns[2])\n", + "\n", + "if compute_momentum:\n", + " p.line(data.index, tmo_main_mom, color=\"orange\", legend_label=data.columns[3])\n", + " p.line(data.index, tmo_smooth_mom, color=\"yellow\", legend_label=data.columns[4])\n", + "\n", + "# Add the histogram, but ommit the label\n", + "hist_source = ColumnDataSource(data=dict(\n", + " x=data.index,\n", + " top=(tmo_main - tmo_smooth) * scaling_factor,\n", + " colors=np.where(tmo_main > tmo_smooth, \"green\", \"red\"),\n", + "))\n", + "p.vbar(x='x', top='top', source=hist_source, width=1, fill_color='colors', fill_alpha=0.3, line_color=None) #, legend_label=\"Main-Smooth Histogram\")\n", + "\n", + "# We can't place a legend patch side by side to the best of my knowledge. We can just not add the legend entries\n", + "# to the N segments, and create 2 invisible segments for red and green, adding the TMO main signal legend entry.\n", + "# It'll have to suffice for the time being.\n", + "green_legend_line = p.line(x=[0, 0], y=[0, 0], color=\"green\", line_width=2, visible=False)\n", + "red_legend_line = p.line(x=[0, 0], y=[0, 0], color=\"red\", line_width=2, visible=False)\n", + "\n", + "custom_legend_item_green = LegendItem(label=data.columns[1], renderers=[green_legend_line])\n", + "custom_legend_item_red = LegendItem(label=data.columns[1], renderers=[red_legend_line])\n", + "\n", + "p.legend[0].items.extend([custom_legend_item_green, custom_legend_item_red])\n", + "output_notebook()\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "id": "2f411c50-17f1-49bb-800c-274f767dd924", + "metadata": {}, + "source": [ + "### TMO: combining with other indicators\n", + "\n", + "Other indicators combine TMO with, for example, John Carter's [TTM Squeeze](https://school.stockcharts.com/doku.php?id=technical_indicators:ttm_squeeze) indicator, as seen [here](https://www.tradingview.com/script/HibAeQPA-TMO-with-TTM-Squeeze/)\n", + "The ability to control the traces and legends dynamically, zoom-in/out, pan, makes the other plotting alternatives to matplotlib, solid propositions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ab98a5d-44c0-4fb2-b6ff-2de229143e31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pandas_ta/core.py b/pandas_ta/core.py index 21ffda3a..b9a6a7ab 100644 --- a/pandas_ta/core.py +++ b/pandas_ta/core.py @@ -1117,6 +1117,12 @@ def td_seq(self, asint=None, offset=None, show_all=None, **kwargs: DictLike): result = td_seq(close=close, asint=asint, offset=offset, show_all=show_all, **kwargs) return self._post_process(result, **kwargs) + def tmo(self, tmo_length=None, calc_length=None, smooth_length=None, mamode=None, compute_momentum=False, normalize_signal=False, offset=None, **kwargs: DictLike): + open_ = self._get_column(kwargs.pop("open", "open")) + close = self._get_column(kwargs.pop("close", "close")) + result = tmo(open_, close, tmo_length=tmo_length, calc_length=calc_length, smooth_length=smooth_length, mamode=mamode, compute_momentum=compute_momentum, normalize_signal=normalize_signal, offset=offset, **kwargs) + return self._post_process(result, **kwargs) + def trix(self, length=None, signal=None, scalar=None, drift=None, offset=None, **kwargs: DictLike): close = self._get_column(kwargs.pop("close", "close")) result = trix(close=close, length=length, signal=signal, scalar=scalar, drift=drift, offset=offset, **kwargs) diff --git a/pandas_ta/maps.py b/pandas_ta/maps.py index 406fa0b0..4ecc7a5e 100644 --- a/pandas_ta/maps.py +++ b/pandas_ta/maps.py @@ -52,7 +52,7 @@ "coppock", "cti", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "qqe", "roc", "rsi", "rsx", "rvgi", "slope", "smi", "squeeze", "squeeze_pro", "stc", "stoch", - "stochf", "stochrsi", "td_seq", "trix", "tsi", "uo", "willr" + "stochf", "stochrsi", "td_seq", "tmo", "trix", "tsi", "uo", "willr" ], # Overlap "overlap": [ diff --git a/pandas_ta/momentum/__init__.py b/pandas_ta/momentum/__init__.py index b7e78f71..f2930a76 100644 --- a/pandas_ta/momentum/__init__.py +++ b/pandas_ta/momentum/__init__.py @@ -36,6 +36,7 @@ from .stochf import stochf from .stochrsi import stochrsi from .td_seq import td_seq +from .tmo import tmo from .trix import trix from .tsi import tsi from .uo import uo @@ -79,6 +80,7 @@ "stochf", "stochrsi", "td_seq", + "tmo", "trix", "tsi", "uo", diff --git a/pandas_ta/momentum/tmo.py b/pandas_ta/momentum/tmo.py new file mode 100644 index 00000000..7b1aac4b --- /dev/null +++ b/pandas_ta/momentum/tmo.py @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +from pandas import DataFrame, Series +from pandas_ta.overlap import ma +from pandas_ta.utils import get_offset, verify_series + + +def tmo(open_, close, tmo_length=None, calc_length=None, smooth_length=None, mamode=None, + compute_momentum=False, normalize_signal=False, offset=None, **kwargs): + """True Momentum Oscillator (TMO) + + The True Momentum Oscillator (TMO) is an indicator that aims to capture the + true momentum underlying the price movement of an asset over a specified time + frame. It quantifies the net buying and selling pressure by summing and then + smoothing the signum of the closing and opening price difference over the + given period, and then computing a main and smooth signal with a series of + moving averages. + Crossovers between the main and smoth signal generate potential signals for + buying and selling opportunities. + Some platforms present versions of this indicator with an optional momentum + calculation for the main TMO signal and its smooth version, as well as the + possibility to normalize the signals to the [-100,100] range, which has the + added benefit of allowing the definition of overbought and oversold regions, + typically -70 and 70. + + Calculation: + Default Inputs: `tmo_length=14, calc_length=5, smooth_length=3` + + EMA = Exponential Moving Average + Delta = close - open + Signum = 1 if Delta > 0, 0 if Delta = 0, -1 if Delta < 0 + SUM = Summation of N given values + MA = EMA(SUM(Delta, tmo_length), calc_length) + TMO = EMA(MA, smooth_length) + TMOs = EMA(TMO, smooth_length) + TMO mom = TMO - TMO[-tmo_length] + TMOs mom = TMOs - TMOs[-tmo_length] + + Sources: + https://www.tradingview.com/script/VRwDppqd-True-Momentum-Oscillator/ + https://www.tradingview.com/script/65vpO7T5-True-Momentum-Oscillator-Universal-Edition/ + https://www.tradingview.com/script/o9BQyaA4-True-Momentum-Oscillator/ + + Args: + open_ (pd.Series): Series of 'open' prices. + close (pd.Series): Series of 'close' prices. + tmo_length (int): The period for TMO calculation. Default: 14 + calc_length (int): Initial moving average window. Default: 5 + smooth_length (int): Main and smooth signal MA window. Default: 3 + mamode (str): See ``help(ta.ma)``. Default: 'ema' + compute_momentum (bool): Compute main and smooth momentum. Default: False + normalize_signal (bool): Normalize TMO values to [-100,100]. Default: False + offset (int): How many periods to offset the result. Default: 0 + + Kwargs: + fillna (value, optional): pd.DataFrame.fillna(value) + fill_method (value, optional): Type of fill method + + Returns: + pd.Series: main signal, smooth signal, main momentum, smooth momentum + """ + + # Validate + tmo_length = int(tmo_length) if tmo_length and tmo_length > 0 else 14 + calc_length = int(calc_length) if calc_length and calc_length > 0 else 5 + smooth_length = int(smooth_length) if smooth_length and smooth_length > 0 else 3 + mamode = mamode if isinstance(mamode, str) else "ema" + compute_momentum = compute_momentum if isinstance(compute_momentum, bool) else False + normalize_signal = normalize_signal if isinstance(normalize_signal, bool) else False + + open_ = verify_series(open_, max(tmo_length, calc_length, smooth_length)) + close = verify_series(close, max(tmo_length, calc_length, smooth_length)) + offset = get_offset(offset) + + if open_ is None or close is None: + return None + + # Calculate (see documentation) + signum_values = Series(close - open_).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0)) + sum_signum = signum_values.rolling(window=tmo_length).sum() + if normalize_signal: + sum_signum = sum_signum * 100 / tmo_length + + initial_ema = ma(mamode, sum_signum, length=calc_length) + main_signal = ma(mamode, initial_ema, length=smooth_length) + smooth_signal = ma(mamode, main_signal, length=smooth_length) + + if compute_momentum: + mom_main = main_signal - main_signal.shift(tmo_length) + mom_smooth = smooth_signal - smooth_signal.shift(tmo_length) + else: + mom_main = Series([0] * len(main_signal), index=main_signal.index) + mom_smooth = Series([0] * len(smooth_signal), index=smooth_signal.index) + + # Offset + if offset != 0: + main_signal = main_signal.shift(offset) + smooth_signal = smooth_signal.shift(offset) + mom_main = mom_main.shift(offset) + mom_smooth = mom_smooth.shift(offset) + + # Fill + fill_value = kwargs.get("fillna", None) + fill_method = kwargs.get("fill_method", None) + + if fill_value is not None: + main_signal.fillna(fill_value, inplace=True) + smooth_signal.fillna(fill_value, inplace=True) + mom_main.fillna(fill_value, inplace=True) + mom_smooth.fillna(fill_value, inplace=True) + + if fill_method is not None: + main_signal.fillna(method=fill_method, inplace=True) + smooth_signal.fillna(method=fill_method, inplace=True) + mom_main.fillna(fill_value, inplace=True) + mom_smooth.fillna(fill_value, inplace=True) + + # Name and Category + tmo_category = "momentum" + params = f"{tmo_length}_{calc_length}_{smooth_length}" + + df = DataFrame({ + f"TMO_{params}": main_signal, + f"TMO_Smooth_{params}": smooth_signal, + f"TMO_Main_Mom_{params}": mom_main, + f"TMO_Smooth_Mom_{params}": mom_smooth + }) + + df.name = f"TMO_{params}" + df.category = tmo_category + + return df \ No newline at end of file diff --git a/tests/test_indicator_momentum.py b/tests/test_indicator_momentum.py index 4b2d5ba3..ae6bc305 100644 --- a/tests/test_indicator_momentum.py +++ b/tests/test_indicator_momentum.py @@ -515,6 +515,10 @@ def test_stochrsi(df): except Exception as ex: error_analysis(result.iloc[:, 0], CORRELATION, ex, newline=False) +def test_tmo(self): + result = ta.tmo(df.open, df.close) + self.assertIsInstance(result, DataFrame) + self.assertEqual(result.name, "TMO_14_5_3") def test_trix(df): result = ta.trix(df.close) @@ -772,6 +776,12 @@ def test_ext_td_seq(df): assert list(df.columns[-2:]) == ["TD_SEQ_UPa", "TD_SEQ_DNa"] +def test_tmo(self): + result = pandas_ta.tmo(self.open, self.close) + self.assertIsInstance(result, DataFrame) + self.assertEqual(result.name, "TMO_14_5_3") + + def test_ext_trix(df): df.ta.trix(append=True) assert list(df.columns[-2:]) == ["TRIX_30_9", "TRIXs_30_9"]