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quant/vectorbt/tests/notebooks/indicators.ipynb
bingyi bbfda1446c 增加vectorbt源码
2025-11-01 09:32:26 +08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# indicators"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import vectorbt as vbt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from datetime import datetime, timedelta\n",
"from numba import njit\n",
"import itertools\n",
"import talib\n",
"import ta"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Disable caching for performance testing\n",
"vbt.settings.caching['enabled'] = False"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"close = pd.DataFrame({\n",
" 'a': [1., 2., 3., 4., 5.],\n",
" 'b': [5., 4., 3., 2., 1.],\n",
" 'c': [1., 2., 3., 2., 1.]\n",
"}, index=pd.DatetimeIndex([\n",
" datetime(2018, 1, 1),\n",
" datetime(2018, 1, 2),\n",
" datetime(2018, 1, 3),\n",
" datetime(2018, 1, 4),\n",
" datetime(2018, 1, 5)\n",
"]))\n",
"np.random.seed(42)\n",
"high = close * np.random.uniform(1, 1.1, size=close.shape)\n",
"low = close * np.random.uniform(0.9, 1, size=close.shape)\n",
"volume = close * 0 + np.random.randint(1, 10, size=close.shape).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"big_close = pd.DataFrame(np.random.randint(10, size=(1000, 1000)).astype(float))\n",
"big_close.index = [datetime(2018, 1, 1) + timedelta(days=i) for i in range(1000)]\n",
"big_high = big_close * np.random.uniform(1, 1.1, size=big_close.shape)\n",
"big_low = big_close * np.random.uniform(0.9, 1, size=big_close.shape)\n",
"big_volume = big_close * 0 + np.random.randint(10, 100, size=big_close.shape).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"close_ts = pd.Series([1, 2, 3, 4, 3, 2, 1], index=pd.DatetimeIndex([\n",
" datetime(2018, 1, 1),\n",
" datetime(2018, 1, 2),\n",
" datetime(2018, 1, 3),\n",
" datetime(2018, 1, 4),\n",
" datetime(2018, 1, 5),\n",
" datetime(2018, 1, 6),\n",
" datetime(2018, 1, 7)\n",
"]))\n",
"high_ts = close_ts * 1.1\n",
"low_ts = close_ts * 0.9\n",
"volume_ts = pd.Series([4, 3, 2, 1, 2, 3, 4], index=close_ts.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IndicatorFactory"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0\n",
"2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0\n",
"2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0\n",
"2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0\n",
"2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0\n",
"2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0\n",
"2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0\n",
"2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0\n",
"2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0\n"
]
}
],
"source": [
"def apply_func(i, ts, p, a, b=100):\n",
" return ts * p[i] + a + b\n",
"\n",
"@njit\n",
"def apply_func_nb(i, ts, p, a, b):\n",
" return ts * p[i] + a + b # numba doesn't support **kwargs\n",
"\n",
"# Custom function can be anything that takes time series, params and other arguments, and returns outputs\n",
"def custom_func(ts, p, *args, **kwargs):\n",
" return vbt.base.combine_fns.apply_and_concat_one(len(p), apply_func, ts, p, *args, **kwargs)\n",
"\n",
"@njit\n",
"def custom_func_nb(ts, p, *args):\n",
" return vbt.base.combine_fns.apply_and_concat_one_nb(len(p), apply_func_nb, ts, p, *args)\n",
"\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_custom_func(custom_func, var_args=True)\n",
" .run(close, [0, 1], 10, b=100).out)\n",
"print(F.from_custom_func(custom_func_nb, var_args=True)\n",
" .run(close, [0, 1], 10, 100).out)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0\n",
"2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0\n",
"2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0\n",
"2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0\n",
"2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0\n",
"2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0\n",
"2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0\n",
"2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0\n",
"2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0\n"
]
}
],
"source": [
"# Apply function is performed on each parameter individually, and each output is then stacked for you\n",
"# Apply functions are less customizable than custom functions, but are simpler to write\n",
"def apply_func(ts, p, a, b=100):\n",
" return ts * p + a + b\n",
"\n",
"@njit\n",
"def apply_func_nb(ts, p, a, b):\n",
" return ts * p + a + b # numba doesn't support **kwargs\n",
" \n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(apply_func, var_args=True)\n",
" .run(close, [0, 1], 10, b=100).out)\n",
"print(F.from_apply_func(apply_func_nb, var_args=True)\n",
" .run(close, [0, 1], 10, 100).out)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 2 \n",
" a b c a b c a b c\n",
"2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0\n",
"2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0\n",
"2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0\n",
"2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0\n",
"custom_p 0 1 2 \n",
" a b c a b c a b c\n",
"2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0\n",
"2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0\n",
"2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0\n",
"2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0\n"
]
}
],
"source": [
"# test *args\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p, a: ts * p + a, var_args=True)\n",
" .run(close, [0, 1, 2], 3).out) \n",
"print(F.from_apply_func(njit(lambda ts, p, a: ts * p + a), var_args=True)\n",
" .run(close, [0, 1, 2], 3).out)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 2 \n",
" a b c a b c a b c\n",
"2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0\n",
"2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0\n",
"2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0\n",
"2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0\n"
]
}
],
"source": [
"# test **kwargs\n",
"# Numba doesn't support kwargs out of the box\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p, a=1: ts * p + a)\n",
" .run(close, [0, 1, 2], a=3).out) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" 0 1 2 0 1 2\n",
"0 0 0 0 1 1 1\n",
"1 0 0 0 1 1 1\n",
"2 0 0 0 1 1 1\n",
"custom_p 0 1 \n",
" 0 1 2 0 1 2\n",
"0 0 0 0 1 1 1\n",
"1 0 0 0 1 1 1\n",
"2 0 0 0 1 1 1\n"
]
}
],
"source": [
"# test no inputs\n",
"F = vbt.IndicatorFactory(param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda p: np.full((3, 3), p))\n",
" .run([0, 1]).out)\n",
"print(F.from_apply_func(njit(lambda p: np.full((3, 3), p)))\n",
" .run([0, 1]).out)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"dtype: int64\n",
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"dtype: int64\n",
"custom_p 0 1\n",
"0 0 1\n",
"1 0 1\n",
"2 0 1\n",
"3 0 1\n",
"4 0 1\n",
"custom_p 0 1\n",
"0 0 1\n",
"1 0 1\n",
"2 0 1\n",
"3 0 1\n",
"4 0 1\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0 0 0 1 1 1\n",
"2018-01-02 0 0 0 1 1 1\n",
"2018-01-03 0 0 0 1 1 1\n",
"2018-01-04 0 0 0 1 1 1\n",
"2018-01-05 0 0 0 1 1 1\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0 0 0 1 1 1\n",
"2018-01-02 0 0 0 1 1 1\n",
"2018-01-03 0 0 0 1 1 1\n",
"2018-01-04 0 0 0 1 1 1\n",
"2018-01-05 0 0 0 1 1 1\n"
]
}
],
"source": [
"# test no inputs with input_shape, input_index and input_columns\n",
"F = vbt.IndicatorFactory(param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)\n",
" .run((5,), 0).out)\n",
"print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)\n",
" .run((5,), 0).out)\n",
"\n",
"print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)\n",
" .run((5,), [0, 1]).out)\n",
"print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)\n",
" .run((5,), [0, 1]).out)\n",
"\n",
"print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)\n",
" .run((5, 3), [0, 1], input_index=close.index, input_columns=close.columns).out)\n",
"print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)\n",
" .run((5, 3), [0, 1], input_index=close.index, input_columns=close.columns).out)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.037454 27.376786 1.073199\n",
"2018-01-02 0.0 0.0 0.0 4.239463 16.249630 4.062398\n",
"2018-01-03 0.0 0.0 0.0 9.052275 9.779559 9.541004\n",
"2018-01-04 0.0 0.0 0.0 17.132916 4.008234 4.387964\n",
"2018-01-05 0.0 0.0 0.0 27.081107 1.021234 1.018182\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.037454 27.376786 1.073199\n",
"2018-01-02 0.0 0.0 0.0 4.239463 16.249630 4.062398\n",
"2018-01-03 0.0 0.0 0.0 9.052275 9.779559 9.541004\n",
"2018-01-04 0.0 0.0 0.0 17.132916 4.008234 4.387964\n",
"2018-01-05 0.0 0.0 0.0 27.081107 1.021234 1.018182\n"
]
}
],
"source": [
"# test multiple inputs\n",
"F = vbt.IndicatorFactory(input_names=['ts1', 'ts2'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts1, ts2, p: ts1 * ts2 * p)\n",
" .run(close, high, [0, 1]).out)\n",
"print(F.from_apply_func(njit(lambda ts1, ts2, p: ts1 * ts2 * p))\n",
" .run(close, high, [0, 1]).out)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n"
]
}
],
"source": [
"# test no params\n",
"F = vbt.IndicatorFactory(input_names=['ts'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts: ts)\n",
" .run(close).out)\n",
"print(F.from_apply_func(njit(lambda ts: ts))\n",
" .run(close).out)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2\n",
"0 1 1 1\n",
"1 1 1 1\n",
"2 1 1 1\n",
" 0 1 2\n",
"0 1 1 1\n",
"1 1 1 1\n",
"2 1 1 1\n"
]
}
],
"source": [
"# test no inputs and no params\n",
"F = vbt.IndicatorFactory(output_names=['out'])\n",
"print(F.from_apply_func(lambda: np.full((3, 3), 1))\n",
" .run().out)\n",
"print(F.from_apply_func(njit(lambda: np.full((3, 3), 1)))\n",
" .run().out)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 0 1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 4.0 20.0 4.0\n",
"2018-01-02 4.0 8.0 4.0 8.0 16.0 8.0\n",
"2018-01-03 6.0 6.0 6.0 12.0 12.0 12.0\n",
"2018-01-04 8.0 4.0 4.0 16.0 8.0 8.0\n",
"2018-01-05 10.0 2.0 2.0 20.0 4.0 4.0\n",
"custom_p1 0 1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 4.0 20.0 4.0\n",
"2018-01-02 4.0 8.0 4.0 8.0 16.0 8.0\n",
"2018-01-03 6.0 6.0 6.0 12.0 12.0 12.0\n",
"2018-01-04 8.0 4.0 4.0 16.0 8.0 8.0\n",
"2018-01-05 10.0 2.0 2.0 20.0 4.0 4.0\n"
]
}
],
"source": [
"# test multiple params\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))\n",
" .run(close, np.asarray([0, 1]), np.asarray([2, 3])).out) \n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))\n",
" .run(close, np.asarray([0, 1]), np.asarray([2, 3])).out)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 array_0 array_1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0\n",
"2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0\n",
"2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0\n",
"2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0\n",
"2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0\n",
"custom_p1 array_0 array_1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0\n",
"2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0\n",
"2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0\n",
"2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0\n",
"2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0\n"
]
}
],
"source": [
"# test param_settings array_like\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), \n",
" param_settings={'p1': {'is_array_like': True}})\n",
" .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out) \n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), \n",
" param_settings={'p1': {'is_array_like': True}})\n",
" .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 array_0 array_1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0\n",
"2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0\n",
"2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0\n",
"2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0\n",
"2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0\n",
"custom_p1 array_0 array_1 \n",
"custom_p2 2 3 \n",
" a b c a b c\n",
"2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0\n",
"2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0\n",
"2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0\n",
"2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0\n",
"2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0\n"
]
}
],
"source": [
"# test param_settings bc_to_input\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), \n",
" param_settings={'p1': {'is_array_like': True, 'bc_to_input': True}})\n",
" .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out) \n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), \n",
" param_settings={'p1': {'is_array_like': True, 'bc_to_input': True}})\n",
" .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 0 1 \\\n",
"custom_p2 2 3 2 3 \n",
" a b c a b c a b c a b \n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 3.0 15.0 3.0 4.0 20.0 \n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 6.0 12.0 6.0 8.0 16.0 \n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 9.0 9.0 9.0 12.0 12.0 \n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 12.0 6.0 6.0 16.0 8.0 \n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 15.0 3.0 3.0 20.0 4.0 \n",
"\n",
"custom_p1 \n",
"custom_p2 \n",
" c \n",
"2018-01-01 4.0 \n",
"2018-01-02 8.0 \n",
"2018-01-03 12.0 \n",
"2018-01-04 8.0 \n",
"2018-01-05 4.0 \n",
"custom_p1 0 1 \\\n",
"custom_p2 2 3 2 3 \n",
" a b c a b c a b c a b \n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 3.0 15.0 3.0 4.0 20.0 \n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 6.0 12.0 6.0 8.0 16.0 \n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 9.0 9.0 9.0 12.0 12.0 \n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 12.0 6.0 6.0 16.0 8.0 \n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 15.0 3.0 3.0 20.0 4.0 \n",
"\n",
"custom_p1 \n",
"custom_p2 \n",
" c \n",
"2018-01-01 4.0 \n",
"2018-01-02 8.0 \n",
"2018-01-03 12.0 \n",
"2018-01-04 8.0 \n",
"2018-01-05 4.0 \n"
]
}
],
"source": [
"# test param product\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))\n",
" .run(close, [0, 1], [2, 3], param_product=True).out) \n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))\n",
" .run(close, [0, 1], [2, 3], param_product=True).out)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n",
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n"
]
}
],
"source": [
"# test default params\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)\n",
" .run(close, [0, 1]).out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)\n",
" .run(close, [0, 1]).out)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n",
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n"
]
}
],
"source": [
"# test hide_params\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), hide_params=['p2'])\n",
" .run(close, [0, 1], 2).out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), hide_params=['p2'])\n",
" .run(close, [0, 1], 2).out)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 0 1 \n",
"custom_p2 2 2 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n",
"custom_p1 0 1 \n",
"custom_p2 2 2 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n",
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n",
"custom_p1 0 1 \n",
" a b c a b c\n",
"2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0\n",
"2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0\n",
"2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0\n",
"2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0\n",
"2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0\n"
]
}
],
"source": [
"# test hide_default\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)\n",
" .run(close, [0, 1], hide_default=False).out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)\n",
" .run(close, [0, 1], hide_default=False).out)\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)\n",
" .run(close, [0, 1], hide_default=True).out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)\n",
" .run(close, [0, 1], hide_default=True).out)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0\n",
"2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0\n",
"2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0\n",
"2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0\n",
"2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0\n",
"2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0\n",
"2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0\n",
"2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0\n",
"2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0\n",
"2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0\n",
"2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0\n",
"2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0\n",
"2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0\n",
"2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0\n",
"2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0\n",
"2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0\n",
"2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0\n"
]
}
],
"source": [
"# test multiple outputs\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['o1', 'o2'])\n",
"print(F.from_apply_func(lambda ts, p: (ts * p, ts * p ** 2))\n",
" .run(close, [0, 1]).o1)\n",
"print(F.from_apply_func(lambda ts, p: (ts * p, ts * p ** 2))\n",
" .run(close, [0, 1]).o2)\n",
"print(F.from_apply_func(njit(lambda ts, p: (ts * p, ts * p ** 2)))\n",
" .run(close, [0, 1]).o1)\n",
"print(F.from_apply_func(njit(lambda ts, p: (ts * p, ts * p ** 2)))\n",
" .run(close, [0, 1]).o2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 0.0 0.0\n",
"2018-01-02 0.0 0.0 0.0 1.0 0.0 0.0\n",
"2018-01-03 0.0 0.0 0.0 1.0 2.0 3.0\n",
"2018-01-04 0.0 5.0 5.0 1.0 3.0 2.0\n",
"2018-01-05 0.0 1.0 2.0 1.0 2.0 1.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0.0 0.0 0.0 1.0 0.0 0.0\n",
"2018-01-02 0.0 0.0 0.0 1.0 0.0 0.0\n",
"2018-01-03 0.0 0.0 0.0 1.0 2.0 3.0\n",
"2018-01-04 0.0 5.0 5.0 1.0 3.0 2.0\n",
"2018-01-05 0.0 1.0 2.0 1.0 2.0 1.0\n",
"custom_p 0 1 \\\n",
" a b c a \n",
"2018-01-01 0 0 0 1 \n",
"2018-01-02 0 0 0 1 \n",
"2018-01-03 0 0 0 1 \n",
"2018-01-04 0 4617315517961601024 4617315517961601024 1 \n",
"2018-01-05 0 4607182418800017408 4611686018427387904 1 \n",
"\n",
"custom_p \n",
" b c \n",
"2018-01-01 0 0 \n",
"2018-01-02 0 0 \n",
"2018-01-03 4611686018427387904 4613937818241073152 \n",
"2018-01-04 4613937818241073152 4611686018427387904 \n",
"2018-01-05 4611686018427387904 4607182418800017408 \n",
"custom_p 0 1 \\\n",
" a b c a \n",
"2018-01-01 0 4616189618054758400 4618441417868443648 1 \n",
"2018-01-02 0 4618441417868443648 4616189618054758400 1 \n",
"2018-01-03 0 4616189618054758400 4611686018427387904 1 \n",
"2018-01-04 0 4624633867356078080 4624633867356078080 1 \n",
"2018-01-05 0 4613937818241073152 4618441417868443648 1 \n",
"\n",
"custom_p \n",
" b c \n",
"2018-01-01 4621819117588971520 4621819117588971520 \n",
"2018-01-02 4611686018427387904 4616189618054758400 \n",
"2018-01-03 4618441417868443648 4621256167635550208 \n",
"2018-01-04 4621256167635550208 4618441417868443648 \n",
"2018-01-05 4618441417868443648 4613937818241073152 \n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0 -1 -1 1 -1 -1\n",
"2018-01-02 0 -1 -1 1 -1 -1\n",
"2018-01-03 0 -1 -1 1 -1 -1\n",
"2018-01-04 0 -1 -1 1 -1 -1\n",
"2018-01-05 0 -1 -1 1 -1 -1\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 0 -1 -1 1 -1 -1\n",
"2018-01-02 0 -1 -1 1 -1 -1\n",
"2018-01-03 0 -1 -1 1 -1 -1\n",
"2018-01-04 0 -1 -1 1 -1 -1\n",
"2018-01-05 0 -1 -1 1 -1 -1\n"
]
}
],
"source": [
"# test in-place outputs\n",
"def apply_func(ts, ts_out, p):\n",
" ts_out[:, 0] = p\n",
" return ts * p\n",
"\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'], in_output_names=['ts_out'])\n",
"print(F.from_apply_func(apply_func)\n",
" .run(close, [0, 1]).ts_out)\n",
"print(F.from_apply_func(njit(apply_func))\n",
" .run(close, [0, 1]).ts_out)\n",
"\n",
"print(F.from_apply_func(apply_func, in_output_settings={'ts_out': {'dtype': np.int64}})\n",
" .run(close, [0, 1]).ts_out)\n",
"print(F.from_apply_func(njit(apply_func), in_output_settings={'ts_out': {'dtype': np.int64}})\n",
" .run(close, [0, 1]).ts_out)\n",
"\n",
"print(F.from_apply_func(apply_func, ts_out=-1)\n",
" .run(close, [0, 1]).ts_out)\n",
"print(F.from_apply_func(njit(apply_func), ts_out=-1)\n",
" .run(close, [0, 1]).ts_out)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 2 \n",
" a b c a b c a b c\n",
"2018-01-01 13.0 13.0 13.0 14.0 18.0 14.0 15.0 23.0 15.0\n",
"2018-01-02 13.0 13.0 13.0 15.0 17.0 15.0 17.0 21.0 17.0\n",
"2018-01-03 13.0 13.0 13.0 16.0 16.0 16.0 19.0 19.0 19.0\n",
"2018-01-04 13.0 13.0 13.0 17.0 15.0 15.0 21.0 17.0 17.0\n",
"2018-01-05 13.0 13.0 13.0 18.0 14.0 14.0 23.0 15.0 15.0\n",
"custom_p 0 1 2 \n",
" a b c a b c a b c\n",
"2018-01-01 13.0 13.0 13.0 14.0 18.0 14.0 15.0 23.0 15.0\n",
"2018-01-02 13.0 13.0 13.0 15.0 17.0 15.0 17.0 21.0 17.0\n",
"2018-01-03 13.0 13.0 13.0 16.0 16.0 16.0 19.0 19.0 19.0\n",
"2018-01-04 13.0 13.0 13.0 17.0 15.0 15.0 21.0 17.0 17.0\n",
"2018-01-05 13.0 13.0 13.0 18.0 14.0 14.0 23.0 15.0 15.0\n"
]
}
],
"source": [
"# test kwargs_to_args\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p, a, kw: ts * p + a + kw, kwargs_to_args=['kw'], var_args=True)\n",
" .run(close, [0, 1, 2], 3, kw=10).out) \n",
"print(F.from_apply_func(njit(lambda ts, p, a, kw: ts * p + a + kw), kwargs_to_args=['kw'], var_args=True)\n",
" .run(close, [0, 1, 2], 3, kw=10).out)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 100.0 100.0 100.0 101.0 105.0 101.0\n",
"2018-01-02 100.0 100.0 100.0 102.0 104.0 102.0\n",
"2018-01-03 100.0 100.0 100.0 103.0 103.0 103.0\n",
"2018-01-04 100.0 100.0 100.0 104.0 102.0 102.0\n",
"2018-01-05 100.0 100.0 100.0 105.0 101.0 101.0\n",
"custom_p 0 1 \n",
" a b c a b c\n",
"2018-01-01 100.0 100.0 100.0 101.0 105.0 101.0\n",
"2018-01-02 100.0 100.0 100.0 102.0 104.0 102.0\n",
"2018-01-03 100.0 100.0 100.0 103.0 103.0 103.0\n",
"2018-01-04 100.0 100.0 100.0 104.0 102.0 102.0\n",
"2018-01-05 100.0 100.0 100.0 105.0 101.0 101.0\n"
]
}
],
"source": [
"# test caching func\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, param, c: ts * param + c, cache_func=lambda ts, params: 100)\n",
" .run(close, [0, 1]).out)\n",
"print(F.from_apply_func(njit(lambda ts, param, c: ts * param + c), cache_func=njit(lambda ts, params: 100))\n",
" .run(close, [0, 1]).out)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"i1_p1 0 1 \n",
"i1_p2 3 4 \n",
" a b c a b c a b c\n",
"2018-01-01 3.0 15.0 3.0 3.0 15.0 3.0 5.0 25.0 5.0\n",
"2018-01-02 6.0 12.0 6.0 6.0 12.0 6.0 10.0 20.0 10.0\n",
"2018-01-03 9.0 9.0 9.0 9.0 9.0 9.0 15.0 15.0 15.0\n",
"2018-01-04 12.0 6.0 6.0 12.0 6.0 6.0 20.0 10.0 10.0\n",
"2018-01-05 15.0 3.0 3.0 15.0 3.0 3.0 25.0 5.0 5.0\n",
"i2_p1 1 2 \n",
"i2_p2 4 5 \n",
" a b c a b c a b c\n",
"2018-01-01 5.0 25.0 5.0 7.0 35.0 7.0 7.0 35.0 7.0\n",
"2018-01-02 10.0 20.0 10.0 14.0 28.0 14.0 14.0 28.0 14.0\n",
"2018-01-03 15.0 15.0 15.0 21.0 21.0 21.0 21.0 21.0 21.0\n",
"2018-01-04 20.0 10.0 10.0 28.0 14.0 14.0 28.0 14.0 14.0\n",
"2018-01-05 25.0 5.0 5.0 35.0 7.0 7.0 35.0 7.0 7.0\n",
"i1_p1 0 1 \n",
"i1_p2 3 4 \n",
" a b c a b c a b c\n",
"2018-01-01 3.0 15.0 3.0 3.0 15.0 3.0 5.0 25.0 5.0\n",
"2018-01-02 6.0 12.0 6.0 6.0 12.0 6.0 10.0 20.0 10.0\n",
"2018-01-03 9.0 9.0 9.0 9.0 9.0 9.0 15.0 15.0 15.0\n",
"2018-01-04 12.0 6.0 6.0 12.0 6.0 6.0 20.0 10.0 10.0\n",
"2018-01-05 15.0 3.0 3.0 15.0 3.0 3.0 25.0 5.0 5.0\n",
"i2_p1 1 2 \n",
"i2_p2 4 5 \n",
" a b c a b c a b c\n",
"2018-01-01 5.0 25.0 5.0 7.0 35.0 7.0 7.0 35.0 7.0\n",
"2018-01-02 10.0 20.0 10.0 14.0 28.0 14.0 14.0 28.0 14.0\n",
"2018-01-03 15.0 15.0 15.0 21.0 21.0 21.0 21.0 21.0 21.0\n",
"2018-01-04 20.0 10.0 10.0 28.0 14.0 14.0 28.0 14.0 14.0\n",
"2018-01-05 25.0 5.0 5.0 35.0 7.0 7.0 35.0 7.0 7.0\n"
]
}
],
"source": [
"# test run_combs\n",
"F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))\n",
" .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[0].out)\n",
"print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))\n",
" .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[1].out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))\n",
" .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[0].out)\n",
"print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))\n",
" .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[1].out)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__annotations__',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" '_config',\n",
" '_iloc',\n",
" '_in_output_names',\n",
" '_indexing_kwargs',\n",
" '_input_mapper',\n",
" '_input_names',\n",
" '_level_names',\n",
" '_loc',\n",
" '_metrics',\n",
" '_o1',\n",
" '_o2',\n",
" '_output_flags',\n",
" '_output_names',\n",
" '_param_names',\n",
" '_run',\n",
" '_run_combs',\n",
" '_short_name',\n",
" '_ts',\n",
" '_ts_out',\n",
" '_wrapper',\n",
" 'apply_func',\n",
" 'build_metrics_doc',\n",
" 'config',\n",
" 'copy',\n",
" 'custom_func',\n",
" 'deep_getattr',\n",
" 'dumps',\n",
" 'iloc',\n",
" 'in_output_names',\n",
" 'indexing_func',\n",
" 'indexing_kwargs',\n",
" 'input_names',\n",
" 'level_names',\n",
" 'load',\n",
" 'loads',\n",
" 'loc',\n",
" 'metrics',\n",
" 'o1',\n",
" 'o1_above',\n",
" 'o1_below',\n",
" 'o1_equal',\n",
" 'o1_stats',\n",
" 'o2',\n",
" 'o2_and',\n",
" 'o2_or',\n",
" 'o2_stats',\n",
" 'o2_xor',\n",
" 'output_flags',\n",
" 'output_names',\n",
" 'override_metrics_doc',\n",
" 'param_names',\n",
" 'post_resolve_attr',\n",
" 'pre_resolve_attr',\n",
" 'regroup',\n",
" 'resolve_attr',\n",
" 'resolve_self',\n",
" 'run',\n",
" 'run_combs',\n",
" 'save',\n",
" 'select_one',\n",
" 'select_one_from_obj',\n",
" 'self_aliases',\n",
" 'short_name',\n",
" 'stats',\n",
" 'stats_defaults',\n",
" 'to_doc',\n",
" 'ts',\n",
" 'ts_above',\n",
" 'ts_below',\n",
" 'ts_equal',\n",
" 'ts_out',\n",
" 'ts_out_readable',\n",
" 'ts_out_stats',\n",
" 'ts_stats',\n",
" 'update_config',\n",
" 'wrapper',\n",
" 'writeable_attrs',\n",
" 'xs']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import namedtuple\n",
"\n",
"TestEnum = namedtuple('TestEnum', ['Hello', 'World'])(0, 1)\n",
"# test attr_settings\n",
"F = vbt.IndicatorFactory(\n",
" input_names=['ts'], output_names=['o1', 'o2'], in_output_names=['ts_out'],\n",
" attr_settings={\n",
" 'ts': {'dtype': None}, \n",
" 'o1': {'dtype': np.float64}, \n",
" 'o2': {'dtype': np.bool_}, \n",
" 'ts_out': {'dtype': TestEnum}\n",
" }\n",
")\n",
"dir(F.from_apply_func(lambda ts, ts_out: (ts + ts_out, ts + ts_out)).run(close))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"CustomInd = vbt.IndicatorFactory(\n",
" input_names=['ts1', 'ts2'],\n",
" param_names=['p1', 'p2'],\n",
" output_names=['o1', 'o2']\n",
").from_apply_func(lambda ts1, ts2, p1, p2: (ts1 * p1, ts2 * p2))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__annotations__',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" '_in_output_names',\n",
" '_input_names',\n",
" '_metrics',\n",
" '_output_flags',\n",
" '_output_names',\n",
" '_param_names',\n",
" '_run',\n",
" '_run_combs',\n",
" 'apply_func',\n",
" 'build_metrics_doc',\n",
" 'config',\n",
" 'copy',\n",
" 'custom_func',\n",
" 'deep_getattr',\n",
" 'dumps',\n",
" 'iloc',\n",
" 'in_output_names',\n",
" 'indexing_func',\n",
" 'indexing_kwargs',\n",
" 'input_names',\n",
" 'level_names',\n",
" 'load',\n",
" 'loads',\n",
" 'loc',\n",
" 'metrics',\n",
" 'o1',\n",
" 'o1_above',\n",
" 'o1_below',\n",
" 'o1_equal',\n",
" 'o1_stats',\n",
" 'o2',\n",
" 'o2_above',\n",
" 'o2_below',\n",
" 'o2_equal',\n",
" 'o2_stats',\n",
" 'output_flags',\n",
" 'output_names',\n",
" 'override_metrics_doc',\n",
" 'p1_list',\n",
" 'p1_loc',\n",
" 'p2_list',\n",
" 'p2_loc',\n",
" 'param_names',\n",
" 'post_resolve_attr',\n",
" 'pre_resolve_attr',\n",
" 'regroup',\n",
" 'resolve_attr',\n",
" 'resolve_self',\n",
" 'run',\n",
" 'run_combs',\n",
" 'save',\n",
" 'select_one',\n",
" 'select_one_from_obj',\n",
" 'self_aliases',\n",
" 'short_name',\n",
" 'stats',\n",
" 'stats_defaults',\n",
" 'to_doc',\n",
" 'ts1',\n",
" 'ts1_above',\n",
" 'ts1_below',\n",
" 'ts1_equal',\n",
" 'ts1_stats',\n",
" 'ts2',\n",
" 'ts2_above',\n",
" 'ts2_below',\n",
" 'ts2_equal',\n",
" 'ts2_stats',\n",
" 'tuple_loc',\n",
" 'update_config',\n",
" 'wrapper',\n",
" 'writeable_attrs',\n",
" 'xs']"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(CustomInd) # you can list here all of the available tools"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"custom_ind = CustomInd.run(close, high, [1, 2], [3, 4])\n",
"big_custom_ind = CustomInd.run(big_close, big_high, [1, 2], [3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
" '2018-01-05'],\n",
" dtype='datetime64[ns]', freq=None)\n",
"MultiIndex([(1, 3, 'a'),\n",
" (1, 3, 'b'),\n",
" (1, 3, 'c'),\n",
" (2, 4, 'a'),\n",
" (2, 4, 'b'),\n",
" (2, 4, 'c')],\n",
" names=['custom_p1', 'custom_p2', None])\n",
"2\n",
"(5, 6)\n",
"1 days 00:00:00\n"
]
}
],
"source": [
"print(custom_ind.wrapper.index) # subclasses ArrayWrapper\n",
"print(custom_ind.wrapper.columns)\n",
"print(custom_ind.wrapper.ndim)\n",
"print(custom_ind.wrapper.shape)\n",
"print(custom_ind.wrapper.freq)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom\n",
"('custom_p1', 'custom_p2')\n",
"('ts1', 'ts2')\n",
"('p1', 'p2')\n",
"('o1', 'o2')\n",
"{}\n",
"[1, 2]\n",
"[3, 4]\n"
]
}
],
"source": [
"# not changed during indexing\n",
"print(custom_ind.short_name)\n",
"print(custom_ind.level_names)\n",
"print(custom_ind.input_names)\n",
"print(custom_ind.param_names)\n",
"print(custom_ind.output_names)\n",
"print(custom_ind.output_flags)\n",
"print(custom_ind.p1_list)\n",
"print(custom_ind.p2_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pandas indexing"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 5. 1.]\n",
" [2. 4. 2.]\n",
" [3. 3. 3.]\n",
" [4. 2. 2.]\n",
" [5. 1. 1.]]\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0 5.0 1.0 1.0\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"custom_p1 1\n",
"custom_p2 3\n",
" a\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"custom_p1 1\n",
"custom_p2 3\n",
" a\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0\n"
]
}
],
"source": [
"print(custom_ind._ts1)\n",
"print(custom_ind.ts1)\n",
"\n",
"print(custom_ind.ts1.iloc[:, 0])\n",
"print(custom_ind.iloc[:, 0].ts1)\n",
"\n",
"print(custom_ind.ts1.iloc[:, [0]])\n",
"print(custom_ind.iloc[:, [0]].ts1)\n",
"\n",
"print(custom_ind.ts1.iloc[:2, :])\n",
"print(custom_ind.iloc[:2, :].ts1)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"120 µs ± 540 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"1.33 ms ± 40.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1.iloc[:, 0])\n",
"%timeit big_custom_ind.o1.iloc[:, 0] # benchmark, 1 column\n",
"\n",
"print(custom_ind.iloc[:, 0].o1) # performed on the object itself\n",
"%timeit big_custom_ind.iloc[:, 0] # slower since it forwards the operation to each dataframe"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 1 \n",
"custom_p2 3 \n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"910 µs ± 59.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"custom_p1 1 \n",
"custom_p2 3 \n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"18 ms ± 759 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1.iloc[:, np.arange(3)])\n",
"%timeit big_custom_ind.o1.iloc[:, np.arange(1000)] # 1000 columns\n",
"\n",
"print(custom_ind.iloc[:, np.arange(3)].o1)\n",
"%timeit big_custom_ind.iloc[:, np.arange(1000)]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"147 µs ± 270 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"2018-01-01 1.0\n",
"2018-01-02 2.0\n",
"2018-01-03 3.0\n",
"2018-01-04 4.0\n",
"2018-01-05 5.0\n",
"Name: (1, 3, a), dtype: float64\n",
"1.34 ms ± 4.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1.loc[:, (1, 3, 'a')])\n",
"%timeit big_custom_ind.o1.loc[:, (1, 3, 0)] # 1 column\n",
"\n",
"print(custom_ind.loc[:, (1, 3, 'a')].o1)\n",
"%timeit big_custom_ind.loc[:, (1, 3, 0)]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"169 µs ± 1.54 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"6.1 ms ± 75.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1.loc[:, (1, 3)])\n",
"%timeit big_custom_ind.o1.loc[:, 1] # 1000 columns\n",
"\n",
"print(custom_ind.loc[:, (1, 3)].o1)\n",
"%timeit big_custom_ind.loc[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p2 3 \n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"161 µs ± 371 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"custom_p2 3 \n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"6.2 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1.xs(1, axis=1, level=0))\n",
"%timeit big_custom_ind.o1.xs(1, axis=1, level=0) # 1000 columns\n",
"\n",
"print(custom_ind.xs(1, axis=1, level=0).o1)\n",
"%timeit big_custom_ind.xs(1, axis=1, level=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parameter indexing"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([1, 1, 1, 2, 2, 2], dtype='int64', name='custom_p1')\n",
"custom_p2 4 \n",
" a b c\n",
"2018-01-01 2.0 10.0 2.0\n",
"2018-01-02 4.0 8.0 4.0\n",
"2018-01-03 6.0 6.0 6.0\n",
"2018-01-04 8.0 4.0 4.0\n",
"2018-01-05 10.0 2.0 2.0\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 2.0 10.0 2.0\n",
"2018-01-02 2.0 4.0 2.0 4.0 8.0 4.0\n",
"2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0\n",
"2018-01-04 4.0 2.0 2.0 8.0 4.0 4.0\n",
"2018-01-05 5.0 1.0 1.0 10.0 2.0 2.0\n",
"custom_p1 1 \n",
"custom_p2 3 \n",
" a b c a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0 4.0 2.0 2.0 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0 5.0 1.0 1.0 5.0 1.0 1.0\n"
]
}
],
"source": [
"# Indexing by parameter\n",
"print(custom_ind._p1_mapper)\n",
"print(custom_ind.p1_loc[2].o1)\n",
"print(custom_ind.p1_loc[1:2].o1)\n",
"print(custom_ind.p1_loc[[1, 1, 1]].o1)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"18.2 ms ± 1.45 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"122 ms ± 18.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%timeit big_custom_ind.p1_loc[1] # 1000 columns\n",
"%timeit big_custom_ind.p1_loc[np.full(10, 1)] # 10000 columns"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 3), (1, 3), (1, 3), (2, 4), (2, 4), (2, 4)]\n",
" a b c\n",
"2018-01-01 1.0 5.0 1.0\n",
"2018-01-02 2.0 4.0 2.0\n",
"2018-01-03 3.0 3.0 3.0\n",
"2018-01-04 4.0 2.0 2.0\n",
"2018-01-05 5.0 1.0 1.0\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 1.0 5.0 1.0 2.0 10.0 2.0\n",
"2018-01-02 2.0 4.0 2.0 4.0 8.0 4.0\n",
"2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0\n",
"2018-01-04 4.0 2.0 2.0 8.0 4.0 4.0\n",
"2018-01-05 5.0 1.0 1.0 10.0 2.0 2.0\n"
]
}
],
"source": [
"print(custom_ind._tuple_mapper)\n",
"print(custom_ind.tuple_loc[(1, 3)].o1)\n",
"print(custom_ind.tuple_loc[(1, 3):(2, 4)].o1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22.5 ms ± 5.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"172 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%timeit big_custom_ind.tuple_loc[(1, 3)]\n",
"%timeit big_custom_ind.tuple_loc[[(1, 3)] * 10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison methods"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 False True False False True False\n",
"2018-01-02 False True False True True True\n",
"2018-01-03 True True True True True True\n",
"2018-01-04 True False False True True True\n",
"2018-01-05 True False False True False False\n",
"485 µs ± 9.08 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"custom_p1 1 2 \n",
"custom_p2 3 4 \n",
" a b c a b c\n",
"2018-01-01 False True False False True False\n",
"2018-01-02 False True False True True True\n",
"2018-01-03 True True True True True True\n",
"2018-01-04 True False False True True True\n",
"2018-01-05 True False False True False False\n",
"3.32 ms ± 6.22 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"print(custom_ind.o1 > 2)\n",
"%timeit big_custom_ind.o1.values > 2 # don't even try pandas\n",
"\n",
"print(custom_ind.o1_above(2))\n",
"%timeit big_custom_ind.o1_above(2) # slower than numpy because of constructing dataframe"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"custom_p1 1 2 1 \\\n",
"custom_p2 3 4 3 \n",
" a b c a b c a b c \n",
"2018-01-01 False True False False True False False True False \n",
"2018-01-02 False True False True True True False True False \n",
"2018-01-03 True True True True True True False False False \n",
"2018-01-04 True False False True True True True False False \n",
"2018-01-05 True False False True False False True False False \n",
"\n",
"custom_p1 2 \n",
"custom_p2 4 \n",
" a b c \n",
"2018-01-01 False True False \n",
"2018-01-02 True True True \n",
"2018-01-03 True True True \n",
"2018-01-04 True True True \n",
"2018-01-05 True False False \n",
"1.37 ms ± 45.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"custom_o1_above 2 3 \\\n",
"custom_p1 1 2 1 \n",
"custom_p2 3 4 3 \n",
" a b c a b c a b \n",
"2018-01-01 False True False False True False False True \n",
"2018-01-02 False True False True True True False True \n",
"2018-01-03 True True True True True True False False \n",
"2018-01-04 True False False True True True True False \n",
"2018-01-05 True False False True False False True False \n",
"\n",
"custom_o1_above \n",
"custom_p1 2 \n",
"custom_p2 4 \n",
" c a b c \n",
"2018-01-01 False False True False \n",
"2018-01-02 False True True True \n",
"2018-01-03 False True True True \n",
"2018-01-04 False True True True \n",
"2018-01-05 False True False False \n",
"8.31 ms ± 85.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"print(pd.concat((custom_ind.o1 > 2, custom_ind.o1 > 3), axis=1))\n",
"%timeit np.hstack((big_custom_ind.o1.values > 2, big_custom_ind.o1.values > 3))\n",
"\n",
"print(custom_ind.o1_above([2, 3]))\n",
"%timeit big_custom_ind.o1_above([2, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TA-Lib"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"ts = pd.DataFrame({\n",
" 'a': [1, 2, 3, 4, np.nan],\n",
" 'b': [np.nan, 4, 3, 2, 1],\n",
" 'c': [1, 2, np.nan, 2, 1]\n",
"}, index=pd.DatetimeIndex([\n",
" datetime(2018, 1, 1),\n",
" datetime(2018, 1, 2),\n",
" datetime(2018, 1, 3),\n",
" datetime(2018, 1, 4),\n",
" datetime(2018, 1, 5)\n",
"]))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 NaN\n",
"2018-01-02 1.5\n",
"2018-01-03 2.5\n",
"2018-01-04 3.5\n",
"2018-01-05 4.5\n",
"Name: (2, a), dtype: float64\n",
"sma_timeperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5\n",
"sma_timeperiod 2 3 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5 NaN NaN NaN\n",
"2018-01-03 2.5 3.5 2.5 2.0 4.0 2.000000\n",
"2018-01-04 3.5 2.5 2.5 3.0 3.0 2.333333\n",
"2018-01-05 4.5 1.5 1.5 4.0 2.0 2.000000\n"
]
}
],
"source": [
"SMA = vbt.talib('SMA')\n",
"\n",
"print(SMA.run(close['a'], 2).real)\n",
"print(SMA.run(close, 2).real)\n",
"print(SMA.run(close, [2, 3]).real)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13 ms ± 226 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"80.1 ms ± 1.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"99 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"22.3 ms ± 572 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%timeit SMA.run(big_close)\n",
"%timeit SMA.run(big_close, np.arange(2, 10))\n",
"%timeit SMA.run(big_close, np.full(10, 2))\n",
"%timeit SMA.run(big_close, np.full(10, 2), run_unique=True)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3\n",
" 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6\n",
" 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8\n",
" 8 8 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 11 11 11\n",
" 11 11 11 11 11 12 12 12 12 12 12 12 13 13 13 13 13 13 14 14 14 14 14 15\n",
" 15 15 15 16 16 16 17 17 18]\n",
"[ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 4 5 6 7 8 9 10\n",
" 11 12 13 14 15 16 17 18 19 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n",
" 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7 8 9 10 11 12 13 14 15 16\n",
" 17 18 19 8 9 10 11 12 13 14 15 16 17 18 19 9 10 11 12 13 14 15 16 17\n",
" 18 19 10 11 12 13 14 15 16 17 18 19 11 12 13 14 15 16 17 18 19 12 13 14\n",
" 15 16 17 18 19 13 14 15 16 17 18 19 14 15 16 17 18 19 15 16 17 18 19 16\n",
" 17 18 19 17 18 19 18 19 19]\n"
]
}
],
"source": [
"comb = itertools.combinations(np.arange(2, 20), 2)\n",
"fast_windows, slow_windows = np.asarray(list(comb)).transpose()\n",
"print(fast_windows)\n",
"print(slow_windows)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.04 s ± 652 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"1.51 s ± 287 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%timeit SMA.run(big_close, fast_windows), SMA.run(big_close, slow_windows) # individual caching\n",
"%timeit SMA.run_combs(big_close, np.arange(2, 20)) # mutual caching"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.27 s ± 40.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"1.2 s ± 5.87 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%timeit vbt.MA.run(big_close, fast_windows), vbt.MA.run(big_close, slow_windows) # the same using Numba\n",
"%timeit vbt.MA.run_combs(big_close, np.arange(2, 20))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in greater\n",
" result = combine_func(inputs[0], inputs[1], *args, **kwargs)\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in less\n",
" result = combine_func(inputs[0], inputs[1], *args, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sma_1_timeperiod 2 3 \\\n",
"sma_2_timeperiod 3 4 4 \n",
" a b c a b c a b \n",
"2018-01-01 False False False False False False False False \n",
"2018-01-02 False False False False False False False False \n",
"2018-01-03 True False True False False False False False \n",
"2018-01-04 False False False True False True True False \n",
"2018-01-05 False False False False False False False False \n",
"\n",
"sma_1_timeperiod \n",
"sma_2_timeperiod \n",
" c \n",
"2018-01-01 False \n",
"2018-01-02 False \n",
"2018-01-03 False \n",
"2018-01-04 True \n",
"2018-01-05 False \n",
"sma_1_timeperiod 2 3 \\\n",
"sma_2_timeperiod 3 4 4 \n",
" a b c a b c a b \n",
"2018-01-01 False False False False False False False False \n",
"2018-01-02 False False False False False False False False \n",
"2018-01-03 False True False False False False False False \n",
"2018-01-04 False False False False True False False True \n",
"2018-01-05 False False True False False True False False \n",
"\n",
"sma_1_timeperiod \n",
"sma_2_timeperiod \n",
" c \n",
"2018-01-01 False \n",
"2018-01-02 False \n",
"2018-01-03 False \n",
"2018-01-04 False \n",
"2018-01-05 False \n"
]
}
],
"source": [
"sma1, sma2 = SMA.run_combs(close, [2, 3, 4])\n",
"print(sma1.real_crossed_above(sma2))\n",
"print(sma1.real_crossed_below(sma2))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__annotations__',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" '_in_output_names',\n",
" '_input_names',\n",
" '_metrics',\n",
" '_output_flags',\n",
" '_output_names',\n",
" '_param_names',\n",
" '_run',\n",
" '_run_combs',\n",
" 'apply_func',\n",
" 'build_metrics_doc',\n",
" 'close',\n",
" 'close_above',\n",
" 'close_below',\n",
" 'close_equal',\n",
" 'close_stats',\n",
" 'config',\n",
" 'copy',\n",
" 'custom_func',\n",
" 'deep_getattr',\n",
" 'dumps',\n",
" 'iloc',\n",
" 'in_output_names',\n",
" 'indexing_func',\n",
" 'indexing_kwargs',\n",
" 'input_names',\n",
" 'level_names',\n",
" 'load',\n",
" 'loads',\n",
" 'loc',\n",
" 'lowerband',\n",
" 'lowerband_above',\n",
" 'lowerband_below',\n",
" 'lowerband_equal',\n",
" 'lowerband_stats',\n",
" 'matype_list',\n",
" 'matype_loc',\n",
" 'metrics',\n",
" 'middleband',\n",
" 'middleband_above',\n",
" 'middleband_below',\n",
" 'middleband_equal',\n",
" 'middleband_stats',\n",
" 'nbdevdn_list',\n",
" 'nbdevdn_loc',\n",
" 'nbdevup_list',\n",
" 'nbdevup_loc',\n",
" 'output_flags',\n",
" 'output_names',\n",
" 'override_metrics_doc',\n",
" 'param_names',\n",
" 'post_resolve_attr',\n",
" 'pre_resolve_attr',\n",
" 'regroup',\n",
" 'resolve_attr',\n",
" 'resolve_self',\n",
" 'run',\n",
" 'run_combs',\n",
" 'save',\n",
" 'select_one',\n",
" 'select_one_from_obj',\n",
" 'self_aliases',\n",
" 'short_name',\n",
" 'stats',\n",
" 'stats_defaults',\n",
" 'timeperiod_list',\n",
" 'timeperiod_loc',\n",
" 'to_doc',\n",
" 'tuple_loc',\n",
" 'update_config',\n",
" 'upperband',\n",
" 'upperband_above',\n",
" 'upperband_below',\n",
" 'upperband_equal',\n",
" 'upperband_stats',\n",
" 'wrapper',\n",
" 'writeable_attrs',\n",
" 'xs']"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(vbt.talib('BBANDS'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MA"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5\n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 2.428571 3.571429 2.428571\n",
"2018-01-04 3.266667 2.733333 2.200000\n",
"2018-01-05 4.161290 1.838710 1.580645\n",
"sma_timeperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5\n",
"ma_window 2 3 \n",
"ma_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5 NaN NaN NaN\n",
"2018-01-03 2.5 3.5 2.5 2.2500 3.7500 2.2500\n",
"2018-01-04 3.5 2.5 2.5 3.1250 2.8750 2.1250\n",
"2018-01-05 4.5 1.5 1.5 4.0625 1.9375 1.5625\n"
]
}
],
"source": [
"print(close.rolling(2).mean())\n",
"print(close.ewm(span=3, min_periods=3).mean())\n",
"print(vbt.talib('SMA').run(close, timeperiod=2).real)\n",
"print(vbt.MA.run(close, [2, 3], ewm=[False, True]).ma) # adjust=False"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"49 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"12.6 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"7.39 ms ± 58.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"12.5 ms ± 391 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit big_close.rolling(2).mean() # pandas\n",
"%timeit vbt.talib('SMA').run(big_close, timeperiod=2)\n",
"%timeit vbt.MA.run(big_close, 2, return_cache=True) # cache only\n",
"%timeit vbt.MA.run(big_close, 2) # with pre+postprocessing and still beats pandas\n",
"\n",
"print(vbt.MA.run(big_close, 2).ma.shape)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"406 ms ± 1.24 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"68.9 ms ± 347 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"64.6 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"64.3 ms ± 266 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"50.1 ms ± 355 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"37.9 ms ± 3.98 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit pd.concat([big_close.rolling(i).mean() for i in np.arange(2, 10)])\n",
"%timeit vbt.talib('SMA').run(big_close, np.arange(2, 10))\n",
"%timeit vbt.MA.run(big_close, np.arange(2, 10))\n",
"%timeit vbt.MA.run(big_close, np.arange(2, 10), run_unique=True)\n",
"%timeit vbt.MA.run(big_close, np.arange(2, 10), return_cache=True) # cache only\n",
"cache = vbt.MA.run(big_close, np.arange(2, 10), return_cache=True)\n",
"%timeit vbt.MA.run(big_close, np.arange(2, 10), use_cache=cache) # using cache\n",
"\n",
"print(vbt.MA.run(big_close, np.arange(2, 10)).ma.shape)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"517 ms ± 6.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"88.9 ms ± 3.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"25.7 ms ± 61.7 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"26.2 ms ± 188 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"6.61 ms ± 346 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit pd.concat([big_close.rolling(i).mean() for i in np.full(10, 2)])\n",
"%timeit vbt.talib('SMA').run(big_close, np.full(10, 2))\n",
"%timeit vbt.MA.run(big_close, np.full(10, 2))\n",
"%timeit vbt.MA.run(big_close, np.full(10, 2), run_unique=True) # slower for large inputs\n",
"%timeit vbt.MA.run(big_close, np.full(10, 2), return_cache=True)\n",
"\n",
"print(vbt.MA.run(big_close, np.full(10, 2)).ma.shape)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"71.6 ms ± 158 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"10.7 ms ± 127 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"4.51 ms ± 57.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"4.35 ms ± 36 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"613 µs ± 2.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"%timeit pd.concat([big_close.iloc[:, :10].rolling(i).mean() for i in np.full(100, 2)])\n",
"%timeit vbt.talib('SMA').run(big_close.iloc[:, :10], np.full(100, 2))\n",
"%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2))\n",
"%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2), run_unique=True) # faster for smaller inputs\n",
"%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2), return_cache=True)\n",
"\n",
"print(vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2)).ma.shape)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ma_window 2 3 \n",
"ma_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5 NaN NaN NaN\n",
"2018-01-03 2.5 3.5 2.5 2.2500 3.7500 2.2500\n",
"2018-01-04 3.5 2.5 2.5 3.1250 2.8750 2.1250\n",
"2018-01-05 4.5 1.5 1.5 4.0625 1.9375 1.5625\n"
]
}
],
"source": [
"ma = vbt.MA.run(close, [2, 3], ewm=[False, True])\n",
"\n",
"print(ma.ma)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ma[(2, False, 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MSTD"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 0.5 0.5 0.5\n",
"2018-01-03 0.5 0.5 0.5\n",
"2018-01-04 0.5 0.5 0.5\n",
"2018-01-05 0.5 0.5 0.5\n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 0.963624 0.963624 0.963624\n",
"2018-01-04 1.177164 1.177164 0.686607\n",
"2018-01-05 1.345243 1.345243 0.881714\n",
"stddev_timeperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 0.5 0.5 0.5\n",
"2018-01-03 0.5 0.5 0.5\n",
"2018-01-04 0.5 0.5 0.5\n",
"2018-01-05 0.5 0.5 0.5\n",
"mstd_window 2 3 \n",
"mstd_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 0.5 0.5 0.5 NaN NaN NaN\n",
"2018-01-03 0.5 0.5 0.5 1.048809 1.048809 1.048809\n",
"2018-01-04 0.5 0.5 0.5 1.300183 1.300183 0.740013\n",
"2018-01-05 0.5 0.5 0.5 1.469294 1.469294 0.864326\n"
]
}
],
"source": [
"print(close.rolling(2).std(ddof=0))\n",
"print(close.ewm(span=3, min_periods=3).std(ddof=0))\n",
"print(vbt.talib('STDDEV').run(close, timeperiod=2).real) \n",
"print(vbt.MSTD.run(close, [2, 3], ewm=[False, True]).mstd) # adjust=False, ddof=0"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65.2 ms ± 486 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"13.7 ms ± 281 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"11.4 ms ± 669 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit big_close.rolling(2).std()\n",
"%timeit vbt.talib('STDDEV').run(big_close, timeperiod=2)\n",
"%timeit vbt.MSTD.run(big_close, 2)\n",
"\n",
"print(vbt.MSTD.run(big_close, 2).mstd.shape)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"534 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"80.3 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"81.8 ms ± 973 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit pd.concat([big_close.rolling(i).std() for i in np.arange(2, 10)])\n",
"%timeit vbt.talib('STDDEV').run(big_close, timeperiod=np.arange(2, 10))\n",
"%timeit vbt.MSTD.run(big_close, np.arange(2, 10))\n",
"\n",
"print(vbt.MSTD.run(big_close, np.arange(2, 10)).mstd.shape)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"98 ms ± 317 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"27.8 ms ± 44.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('STDDEV').run(big_close, timeperiod=np.full(10, 2))\n",
"%timeit vbt.MSTD.run(big_close, window=np.full(10, 2))\n",
"\n",
"print(vbt.MSTD.run(big_close, window=np.full(10, 2)).close.shape)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mstd_window 2 3 \n",
"mstd_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 0.5 0.5 0.5 NaN NaN NaN\n",
"2018-01-03 0.5 0.5 0.5 1.048809 1.048809 1.048809\n",
"2018-01-04 0.5 0.5 0.5 1.300183 1.300183 0.740013\n",
"2018-01-05 0.5 0.5 0.5 1.469294 1.469294 0.864326\n"
]
}
],
"source": [
"mstd = vbt.MSTD.run(close, [2, 3], [False, True])\n",
"\n",
"print(mstd.mstd)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
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"mstd[(2, False, 'a')].plot().show_svg()"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BBANDS"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 NaN\n",
"2018-01-02 2.5\n",
"2018-01-03 3.5\n",
"2018-01-04 4.5\n",
"2018-01-05 5.5\n",
"Name: (2, 2, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 1.5\n",
"2018-01-03 2.5\n",
"2018-01-04 3.5\n",
"2018-01-05 4.5\n",
"Name: (2, 2, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 0.5\n",
"2018-01-03 1.5\n",
"2018-01-04 2.5\n",
"2018-01-05 3.5\n",
"Name: (2, 2, a), dtype: float64\n",
"bbands_timeperiod 2 \n",
"bbands_nbdevup 2 \n",
"bbands_nbdevdn 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 2.5 5.5 2.5\n",
"2018-01-03 3.5 4.5 3.5\n",
"2018-01-04 4.5 3.5 3.5\n",
"2018-01-05 5.5 2.5 2.5\n",
"bbands_timeperiod 2 \n",
"bbands_nbdevup 2 \n",
"bbands_nbdevdn 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5\n",
"bbands_timeperiod 2 \n",
"bbands_nbdevup 2 \n",
"bbands_nbdevdn 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 0.5 3.5 0.5\n",
"2018-01-03 1.5 2.5 1.5\n",
"2018-01-04 2.5 1.5 1.5\n",
"2018-01-05 3.5 0.5 0.5\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 2.5 5.5 2.5\n",
"2018-01-03 3.5 4.5 3.5\n",
"2018-01-04 4.5 3.5 3.5\n",
"2018-01-05 5.5 2.5 2.5\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 0.5 3.5 0.5\n",
"2018-01-03 1.5 2.5 1.5\n",
"2018-01-04 2.5 1.5 1.5\n",
"2018-01-05 3.5 0.5 0.5\n"
]
}
],
"source": [
"print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_hband)\n",
"print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_mavg)\n",
"print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_lband)\n",
"\n",
"print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).upperband)\n",
"print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).middleband)\n",
"print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).lowerband)\n",
"\n",
"print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).upper)\n",
"print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).middle)\n",
"print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).lower)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23 ms ± 367 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"21.6 ms ± 412 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit vbt.talib('BBANDS').run(big_close, timeperiod=2)\n",
"%timeit vbt.BBANDS.run(big_close, window=2)\n",
"\n",
"print(vbt.BBANDS.run(big_close).close.shape)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"192 ms ± 1.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"185 ms ± 1.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit vbt.talib('BBANDS').run(big_close, timeperiod=np.arange(2, 10))\n",
"%timeit vbt.BBANDS.run(big_close, window=np.arange(2, 10))\n",
"\n",
"print(vbt.BBANDS.run(big_close, window=np.arange(2, 10)).close.shape)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"215 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"100 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('BBANDS').run(big_close, timeperiod=np.full(10, 2))\n",
"%timeit vbt.BBANDS.run(big_close, window=np.full(10, 2))\n",
"\n",
"print(vbt.BBANDS.run(big_close, window=np.full(10, 2)).close.shape)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.5 4.5 1.5 1.5 4.5 1.5\n",
"2018-01-03 2.5 3.5 2.5 2.5 3.5 2.5\n",
"2018-01-04 3.5 2.5 2.5 3.5 2.5 2.5\n",
"2018-01-05 4.5 1.5 1.5 4.5 1.5 1.5\n",
"\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 2.0 5.0 2.0 2.5 5.5 2.5\n",
"2018-01-03 3.0 4.0 3.0 3.5 4.5 3.5\n",
"2018-01-04 4.0 3.0 3.0 4.5 3.5 3.5\n",
"2018-01-05 5.0 2.0 2.0 5.5 2.5 2.5\n",
"\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.0 4.0 1.0 0.5 3.5 0.5\n",
"2018-01-03 2.0 3.0 2.0 1.5 2.5 1.5\n",
"2018-01-04 3.0 2.0 2.0 2.5 1.5 1.5\n",
"2018-01-05 4.0 1.0 1.0 3.5 0.5 0.5\n",
"\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 1.0 0.0 1.0 0.75 0.25 0.75\n",
"2018-01-03 1.0 0.0 1.0 0.75 0.25 0.75\n",
"2018-01-04 1.0 0.0 0.0 0.75 0.25 0.25\n",
"2018-01-05 1.0 0.0 0.0 0.75 0.25 0.25\n",
"\n",
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 0.666667 0.222222 0.666667 1.333333 0.444444 1.333333\n",
"2018-01-03 0.400000 0.285714 0.400000 0.800000 0.571429 0.800000\n",
"2018-01-04 0.285714 0.400000 0.400000 0.571429 0.800000 0.800000\n",
"2018-01-05 0.222222 0.666667 0.666667 0.444444 1.333333 1.333333\n"
]
}
],
"source": [
"bb = vbt.BBANDS.run(close, window=2, alpha=[1., 2.], ewm=False)\n",
"\n",
"print(bb.middle)\n",
"print()\n",
"print(bb.upper)\n",
"print()\n",
"print(bb.lower)\n",
"print()\n",
"print(bb.percent_b)\n",
"print()\n",
"print(bb.bandwidth)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bb_window 2 \n",
"bb_ewm False \n",
"bb_alpha 1.0 2.0 \n",
" a b c a b c\n",
"2018-01-01 False False False False False False\n",
"2018-01-02 False False False True True True\n",
"2018-01-03 False False False True True True\n",
"2018-01-04 False False False True True True\n",
"2018-01-05 False False False True True True\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning:\n",
"\n",
"invalid value encountered in less\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning:\n",
"\n",
"invalid value encountered in greater\n",
"\n"
]
}
],
"source": [
"print(bb.close_below(bb.upper) & bb.close_above(bb.lower)) # price between bands"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
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fill-opacity: 1;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" transform=\"translate(65.75,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"320\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"320\">2018</tspan></text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" transform=\"translate(207.87,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Jan 2</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" transform=\"translate(350,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bb[(2, False, 1., 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RSI"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 NaN\n",
"2018-01-02 100.0\n",
"2018-01-03 100.0\n",
"2018-01-04 100.0\n",
"2018-01-05 100.0\n",
"Name: (2, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 0.0\n",
"2018-01-03 0.0\n",
"2018-01-04 0.0\n",
"2018-01-05 0.0\n",
"Name: (2, b), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 100.000000\n",
"2018-01-03 100.000000\n",
"2018-01-04 42.857143\n",
"2018-01-05 20.000000\n",
"Name: (2, c), dtype: float64\n",
"rsi_timeperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 100.0 0.0 100.0\n",
"2018-01-04 100.0 0.0 50.0\n",
"2018-01-05 100.0 0.0 25.0\n",
"rsi_window 2 \n",
"rsi_ewm True False \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN NaN NaN NaN\n",
"2018-01-03 100.0 0.0 100.000000 100.0 0.0 100.0\n",
"2018-01-04 100.0 0.0 33.333333 100.0 0.0 50.0\n",
"2018-01-05 100.0 0.0 11.111111 100.0 0.0 0.0\n"
]
}
],
"source": [
"print(vbt.ta('RSIIndicator').run(close=close['a'], window=2).rsi) # alpha=1/n\n",
"print(vbt.ta('RSIIndicator').run(close=close['b'], window=2).rsi)\n",
"print(vbt.ta('RSIIndicator').run(close=close['c'], window=2).rsi)\n",
"print(vbt.talib('RSI').run(close, timeperiod=2).real)\n",
"print(vbt.RSI.run(close, window=[2, 2], ewm=[True, False]).rsi) # span=n"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19.4 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"27.6 ms ± 922 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit vbt.talib('RSI').run(big_close, timeperiod=2)\n",
"%timeit vbt.RSI.run(big_close, window=2)\n",
"\n",
"print(vbt.RSI.run(big_close, window=2).rsi.shape)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"128 ms ± 253 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"139 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit vbt.talib('RSI').run(big_close, timeperiod=np.arange(2, 10))\n",
"%timeit vbt.RSI.run(big_close, window=np.arange(2, 10))\n",
"\n",
"print(vbt.RSI.run(big_close, window=np.arange(2, 10)).rsi.shape)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"160 ms ± 214 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"53.6 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('RSI').run(big_close, timeperiod=np.full(10, 2))\n",
"%timeit vbt.RSI.run(big_close, window=np.full(10, 2))\n",
"\n",
"print(vbt.RSI.run(big_close, window=np.full(10, 2)).rsi.shape)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rsi_window 2 3 \n",
"rsi_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN NaN NaN NaN\n",
"2018-01-03 100.0 0.0 100.0 NaN NaN NaN\n",
"2018-01-04 100.0 0.0 50.0 100.0 0.0 50.0\n",
"2018-01-05 100.0 0.0 0.0 100.0 0.0 25.0\n"
]
}
],
"source": [
"rsi = vbt.RSI.run(close, window=[2, 3], ewm=[False, True])\n",
"\n",
"print(rsi.rsi)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rsi_window 2 3 \n",
"rsi_ewm False True \n",
" a b c a b c\n",
"2018-01-01 False False False False False False\n",
"2018-01-02 False False False False False False\n",
"2018-01-03 True False True False False False\n",
"2018-01-04 True False False True False False\n",
"2018-01-05 True False False True False False\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning:\n",
"\n",
"invalid value encountered in greater\n",
"\n"
]
}
],
"source": [
"print(rsi.rsi_above(70))"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
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],
"source": [
"rsi[(2, False, 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## STOCH"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 NaN\n",
"2018-01-02 90.033913\n",
"2018-01-03 98.459370\n",
"2018-01-04 81.624981\n",
"2018-01-05 74.524237\n",
"Name: (2, 3, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 NaN\n",
"2018-01-03 NaN\n",
"2018-01-04 90.039421\n",
"2018-01-05 84.869529\n",
"Name: (2, 3, a), dtype: float64\n",
"stochf_fastk_period 2 \n",
"stochf_fastd_period 3 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 81.624981 3.297837 11.941605\n",
"2018-01-05 74.524237 3.900804 7.395659\n",
"stochf_fastk_period 2 \n",
"stochf_fastd_period 3 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 90.039421 12.025053 64.904657\n",
"2018-01-05 84.869529 7.952381 35.000612\n",
"stoch_k_window 2 \n",
"stoch_d_window 3 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 90.033913 16.118819 97.107794\n",
"2018-01-03 98.459370 16.658503 85.664573\n",
"2018-01-04 81.624981 3.297837 11.941605\n",
"2018-01-05 74.524237 3.900804 7.395659\n",
"stoch_k_window 2 \n",
"stoch_d_window 3 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 90.039421 12.025053 64.904657\n",
"2018-01-05 84.869529 7.952381 35.000612\n"
]
}
],
"source": [
"print(vbt.ta('StochasticOscillator').run(high=high['a'], low=low['a'], close=close['a'], window=2, smooth_window=3).stoch)\n",
"print(vbt.ta('StochasticOscillator').run(high=high['a'], low=low['a'], close=close['a'], window=2, smooth_window=3).stoch_signal)\n",
"print(vbt.talib('STOCHF').run(high, low, close, fastk_period=2, fastd_period=3).fastk)\n",
"print(vbt.talib('STOCHF').run(high, low, close, fastk_period=2, fastd_period=3).fastd)\n",
"print(vbt.STOCH.run(high, low, close, k_window=2, d_window=3).percent_k)\n",
"print(vbt.STOCH.run(high, low, close, k_window=2, d_window=3).percent_d)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"29.4 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"26.6 ms ± 607 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=2)\n",
"%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=2)\n",
"\n",
"print(vbt.STOCH.run(big_high, big_low, big_close, k_window=2).percent_d.shape)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"220 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"283 ms ± 5.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=np.arange(2, 10))\n",
"%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=np.arange(2, 10))\n",
"\n",
"print(vbt.STOCH.run(big_high, big_low, big_close, k_window=np.arange(2, 10)).percent_d.shape)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"264 ms ± 672 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"132 ms ± 519 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=np.full(10, 2))\n",
"%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=np.full(10, 2))\n",
"\n",
"print(vbt.STOCH.run(big_high, big_low, big_close, k_window=np.full(10, 2)).percent_d.shape)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stoch_k_window 2 4 \\\n",
"stoch_d_window 2 2 \n",
"stoch_d_ewm False True \n",
" a b c a b \n",
"2018-01-01 NaN NaN NaN NaN NaN \n",
"2018-01-02 90.033913 16.118819 97.107794 NaN NaN \n",
"2018-01-03 98.459370 16.658503 85.664573 NaN NaN \n",
"2018-01-04 81.624981 3.297837 11.941605 91.582811 1.221173 \n",
"2018-01-05 74.524237 3.900804 7.395659 88.208468 1.313450 \n",
"\n",
"stoch_k_window \n",
"stoch_d_window \n",
"stoch_d_ewm \n",
" c \n",
"2018-01-01 NaN \n",
"2018-01-02 NaN \n",
"2018-01-03 NaN \n",
"2018-01-04 47.019332 \n",
"2018-01-05 4.190157 \n",
"stoch_k_window 2 4 \\\n",
"stoch_d_window 2 2 \n",
"stoch_d_ewm False True \n",
" a b c a b \n",
"2018-01-01 NaN NaN NaN NaN NaN \n",
"2018-01-02 NaN NaN NaN NaN NaN \n",
"2018-01-03 94.246641 16.388661 91.386183 NaN NaN \n",
"2018-01-04 90.042175 9.978170 48.803089 NaN NaN \n",
"2018-01-05 78.074609 3.599321 9.668632 89.333249 1.282691 \n",
"\n",
"stoch_k_window \n",
"stoch_d_window \n",
"stoch_d_ewm \n",
" c \n",
"2018-01-01 NaN \n",
"2018-01-02 NaN \n",
"2018-01-03 NaN \n",
"2018-01-04 NaN \n",
"2018-01-05 18.466549 \n"
]
}
],
"source": [
"stochastic = vbt.STOCH.run(high, low, close, k_window=[2, 4], d_window=2, d_ewm=[False, True])\n",
"\n",
"print(stochastic.percent_k)\n",
"print(stochastic.percent_d)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
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"source": [
"stochastic[(2, 2, False, 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MACD"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 NaN\n",
"2018-01-02 NaN\n",
"2018-01-03 0.305556\n",
"2018-01-04 0.393519\n",
"2018-01-05 0.443673\n",
"Name: (3, 2, 2, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 NaN\n",
"2018-01-03 NaN\n",
"2018-01-04 0.364198\n",
"2018-01-05 0.417181\n",
"Name: (3, 2, 2, a), dtype: float64\n",
"2018-01-01 NaN\n",
"2018-01-02 NaN\n",
"2018-01-03 NaN\n",
"2018-01-04 0.029321\n",
"2018-01-05 0.026492\n",
"Name: (3, 2, 2, a), dtype: float64\n",
"macd_fastperiod 2 \n",
"macd_slowperiod 3 \n",
"macd_signalperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 0.5 -0.5 0.166667\n",
"2018-01-05 0.5 -0.5 -0.111111\n",
"macd_fastperiod 2 \n",
"macd_slowperiod 3 \n",
"macd_signalperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 0.5 -0.5 0.333333\n",
"2018-01-05 0.5 -0.5 0.037037\n",
"macd_fastperiod 2 \n",
"macd_slowperiod 3 \n",
"macd_signalperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 0.0 0.0 -0.166667\n",
"2018-01-05 0.0 0.0 -0.148148\n",
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 \n",
"macd_macd_ewm True \n",
"macd_signal_ewm True \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 0.305556 -0.305556 0.305556\n",
"2018-01-04 0.393519 -0.393519 0.060185\n",
"2018-01-05 0.443673 -0.443673 -0.167438\n",
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 \n",
"macd_macd_ewm True \n",
"macd_signal_ewm True \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 0.364198 -0.364198 0.141975\n",
"2018-01-05 0.417181 -0.417181 -0.064300\n",
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 \n",
"macd_macd_ewm True \n",
"macd_signal_ewm True \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN\n",
"2018-01-04 0.029321 -0.029321 -0.081790\n",
"2018-01-05 0.026492 -0.026492 -0.103138\n"
]
}
],
"source": [
"print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd)\n",
"print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd_signal)\n",
"print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd_diff)\n",
"\n",
"print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macd) # uses sma\n",
"print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macdsignal)\n",
"print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macdhist)\n",
"\n",
"print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).macd)\n",
"print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).signal)\n",
"print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).hist)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"28.4 ms ± 571 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"23.1 ms ± 969 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit vbt.talib('MACD').run(big_close, fastperiod=2)\n",
"%timeit vbt.MACD.run(big_close, fast_window=2)\n",
"\n",
"print(vbt.MACD.run(big_close, fast_window=2).macd.shape)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"217 ms ± 2.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"145 ms ± 364 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit vbt.talib('MACD').run(big_close, fastperiod=np.arange(2, 10))\n",
"%timeit vbt.MACD.run(big_close, fast_window=np.arange(2, 10))\n",
"\n",
"print(vbt.MACD.run(big_close, fast_window=np.arange(2, 10)).macd.shape)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"267 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"134 ms ± 6.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('MACD').run(big_close, fastperiod=np.full(10, 2))\n",
"%timeit vbt.MACD.run(big_close, fast_window=np.full(10, 2))\n",
"\n",
"print(vbt.MACD.run(big_close, fast_window=np.full(10, 2)).macd.shape)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 3 \n",
"macd_macd_ewm True True \n",
"macd_signal_ewm True True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN NaN NaN NaN\n",
"2018-01-03 0.305556 -0.305556 0.305556 0.305556 -0.305556 0.305556\n",
"2018-01-04 0.393519 -0.393519 0.060185 0.393519 -0.393519 0.060185\n",
"2018-01-05 0.443673 -0.443673 -0.167438 0.443673 -0.443673 -0.167438\n",
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 3 \n",
"macd_macd_ewm True True \n",
"macd_signal_ewm True True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN NaN NaN NaN\n",
"2018-01-04 0.364198 -0.364198 0.141975 NaN NaN NaN\n",
"2018-01-05 0.417181 -0.417181 -0.064300 0.396605 -0.396605 0.007716\n",
"macd_fast_window 2 \n",
"macd_slow_window 3 \n",
"macd_signal_window 2 3 \n",
"macd_macd_ewm True True \n",
"macd_signal_ewm True True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN NaN NaN NaN\n",
"2018-01-03 NaN NaN NaN NaN NaN NaN\n",
"2018-01-04 0.029321 -0.029321 -0.081790 NaN NaN NaN\n",
"2018-01-05 0.026492 -0.026492 -0.103138 0.047068 -0.047068 -0.175154\n"
]
}
],
"source": [
"macd = vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=[2, 3], macd_ewm=True, signal_ewm=True)\n",
"\n",
"print(macd.macd)\n",
"print(macd.signal)\n",
"print(macd.hist)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:666: RuntimeWarning:\n",
"\n",
"invalid value encountered in greater\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:667: RuntimeWarning:\n",
"\n",
"invalid value encountered in greater\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:667: RuntimeWarning:\n",
"\n",
"invalid value encountered in less_equal\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:668: RuntimeWarning:\n",
"\n",
"invalid value encountered in less\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:669: RuntimeWarning:\n",
"\n",
"invalid value encountered in less\n",
"\n",
"/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:669: RuntimeWarning:\n",
"\n",
"invalid value encountered in greater_equal\n",
"\n"
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],
"source": [
"macd[(2, 3, 2, True, True, 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ATR"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 0.000000\n",
"2018-01-02 0.619423\n",
"2018-01-03 0.818424\n",
"2018-01-04 1.050826\n",
"2018-01-05 1.233524\n",
"Name: (2, a), dtype: float64\n",
"2018-01-01 0.000000\n",
"2018-01-02 1.053372\n",
"2018-01-03 1.132864\n",
"2018-01-04 1.087915\n",
"2018-01-05 1.064337\n",
"Name: (2, b), dtype: float64\n",
"2018-01-01 0.000000\n",
"2018-01-02 0.575961\n",
"2018-01-03 0.878148\n",
"2018-01-04 1.019107\n",
"2018-01-05 1.057231\n",
"Name: (2, c), dtype: float64\n",
"atr_timeperiod 2 \n",
" a b c\n",
"2018-01-01 NaN NaN NaN\n",
"2018-01-02 NaN NaN NaN\n",
"2018-01-03 1.068578 1.247932 1.105767\n",
"2018-01-04 1.175904 1.145449 1.132916\n",
"2018-01-05 1.296063 1.093104 1.114135\n",
"atr_window 2 3 \n",
"atr_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 0.619423 1.053372 0.575961 NaN NaN NaN\n",
"2018-01-03 1.068578 1.247932 1.105767 0.818424 1.132864 0.878148\n",
"2018-01-04 1.150327 1.127661 1.170200 1.050826 1.087915 1.019107\n",
"2018-01-05 1.349725 1.041862 1.127710 1.233524 1.064337 1.057231\n"
]
}
],
"source": [
"print(vbt.ta('AverageTrueRange').run(high['a'], low['a'], close['a'], window=2).average_true_range)\n",
"print(vbt.ta('AverageTrueRange').run(high['b'], low['b'], close['b'], window=2).average_true_range)\n",
"print(vbt.ta('AverageTrueRange').run(high['c'], low['c'], close['c'], window=2).average_true_range)\n",
"print(vbt.talib('ATR').run(high, low, close, timeperiod=2).real)\n",
"print(vbt.ATR.run(high, low, close, window=[2, 3], ewm=[False, True]).atr)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22.5 ms ± 470 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"33.5 ms ± 340 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"# One window\n",
"%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=2)\n",
"%timeit vbt.ATR.run(big_high, big_low, big_close, window=2)\n",
"\n",
"print(vbt.ATR.run(big_high, big_low, big_close, window=2).atr.shape)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"158 ms ± 695 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"110 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 8000)\n"
]
}
],
"source": [
"# Multiple windows\n",
"%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=np.arange(2, 10))\n",
"%timeit vbt.ATR.run(big_high, big_low, big_close, window=np.arange(2, 10)) # rolling min/max very expensive\n",
"\n",
"print(vbt.ATR.run(big_high, big_low, big_close, window=np.arange(2, 10)).atr.shape)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"197 ms ± 1.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"67.5 ms ± 1.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"(1000, 10000)\n"
]
}
],
"source": [
"# One window repeated\n",
"%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=np.full(10, 2))\n",
"%timeit vbt.ATR.run(big_high, big_low, big_close, window=np.full(10, 2))\n",
"\n",
"print(vbt.ATR.run(big_high, big_low, big_close, window=np.full(10, 2)).atr.shape)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"atr_window 2 3 \n",
"atr_ewm False True \n",
" a b c a b c\n",
"2018-01-01 0.119114 0.823236 0.120724 0.119114 0.823236 0.120724\n",
"2018-01-02 1.119732 1.283508 1.031199 1.119732 1.283508 1.031199\n",
"2018-01-03 1.017425 1.212357 1.180335 1.017425 1.212357 1.180335\n",
"2018-01-04 1.283229 1.042965 1.160065 1.283229 1.042965 1.160065\n",
"2018-01-05 1.416221 1.040759 1.095355 1.416221 1.040759 1.095355\n",
"atr_window 2 3 \n",
"atr_ewm False True \n",
" a b c a b c\n",
"2018-01-01 NaN NaN NaN NaN NaN NaN\n",
"2018-01-02 0.619423 1.053372 0.575961 NaN NaN NaN\n",
"2018-01-03 1.068578 1.247932 1.105767 0.818424 1.132864 0.878148\n",
"2018-01-04 1.150327 1.127661 1.170200 1.050826 1.087915 1.019107\n",
"2018-01-05 1.349725 1.041862 1.127710 1.233524 1.064337 1.057231\n"
]
}
],
"source": [
"atr = vbt.ATR.run(high, low, close, window=[2, 3], ewm=[False, True])\n",
"\n",
"print(atr.tr)\n",
"print(atr.atr)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"atr[(2, False, 'a')].plot().show_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OBV"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-01-01 3.0\n",
"2018-01-02 12.0\n",
"2018-01-03 16.0\n",
"2018-01-04 25.0\n",
"2018-01-05 29.0\n",
"Name: a, dtype: float64\n",
"2018-01-01 7.0\n",
"2018-01-02 0.0\n",
"2018-01-03 -9.0\n",
"2018-01-04 -14.0\n",
"2018-01-05 -21.0\n",
"Name: b, dtype: float64\n",
"2018-01-01 5.0\n",
"2018-01-02 7.0\n",
"2018-01-03 9.0\n",
"2018-01-04 7.0\n",
"2018-01-05 -1.0\n",
"Name: c, dtype: float64\n",
" a b c\n",
"2018-01-01 3.0 7.0 5.0\n",
"2018-01-02 12.0 0.0 7.0\n",
"2018-01-03 16.0 -9.0 9.0\n",
"2018-01-04 25.0 -14.0 7.0\n",
"2018-01-05 29.0 -21.0 -1.0\n",
" a b c\n",
"2018-01-01 3.0 7.0 5.0\n",
"2018-01-02 12.0 0.0 7.0\n",
"2018-01-03 16.0 -9.0 9.0\n",
"2018-01-04 25.0 -14.0 7.0\n",
"2018-01-05 29.0 -21.0 -1.0\n"
]
}
],
"source": [
"print(vbt.ta('OnBalanceVolumeIndicator').run(close['a'], volume['a']).on_balance_volume)\n",
"print(vbt.ta('OnBalanceVolumeIndicator').run(close['b'], volume['b']).on_balance_volume)\n",
"print(vbt.ta('OnBalanceVolumeIndicator').run(close['c'], volume['c']).on_balance_volume)\n",
"print(vbt.talib('OBV').run(close, volume).real)\n",
"print(vbt.OBV.run(close, volume).obv)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19.3 ms ± 549 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"17.1 ms ± 1.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"(1000, 1000)\n"
]
}
],
"source": [
"%timeit vbt.talib('OBV').run(big_close, big_volume)\n",
"%timeit vbt.OBV.run(big_close, big_volume)\n",
"\n",
"print(vbt.OBV.run(big_close, big_volume).obv.shape)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"2018-01-01 3.0 7.0 5.0\n",
"2018-01-02 12.0 0.0 7.0\n",
"2018-01-03 16.0 -9.0 9.0\n",
"2018-01-04 25.0 -14.0 7.0\n",
"2018-01-05 29.0 -21.0 -1.0\n"
]
}
],
"source": [
"obv = vbt.OBV.run(close, volume)\n",
"\n",
"print(obv.obv)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"obv_above 0 5 \n",
" a b c a b c\n",
"2018-01-01 True True True False True False\n",
"2018-01-02 True False True True False True\n",
"2018-01-03 True False True True False True\n",
"2018-01-04 True False True True False True\n",
"2018-01-05 True False False True False False\n"
]
}
],
"source": [
"print(obv.obv_above([0, 5]))"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
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font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">30</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-00e0d4\"><g class=\"clips\"/><clipPath id=\"legend00e0d4\"><rect width=\"71\" height=\"29\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(599,11.779999999999994)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\" width=\"71\" height=\"29\" x=\"0\" y=\"0\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url('#legend00e0d4')\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"obv['a'].plot().show_svg()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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},
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}
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