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Added HMA function #8

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Sham2112 Aug 9, 2020
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57 changes: 42 additions & 15 deletions Trading_Using_CNN.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,36 +2,35 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pandas import DataFrame # specify datatype while passing df object"
"from pandas import DataFrame # specify datatype while passing df object\\"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
" #df = pd.read_csv('WMT.csv')\n",
" #Uncomment this and read the WMT csv file wherever its stored on your local device"
"#df = pd.read_csv('WMT.csv')#Uncomment this and read the WMT csv file wherever its stored on your local device"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 39,
"metadata": {},
"outputs": [
{
"ename": "IndentationError",
"evalue": "expected an indented block (<ipython-input-5-cae2ff3fa008>, line 5)",
"output_type": "error",
"ename": "IndentationError",
"evalue": "expected an indented block (<ipython-input-39-2e1955fcb271>, line 5)",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-5-cae2ff3fa008>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m def cmo():\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-39-2e1955fcb271>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m def cmo():\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
]
}
],
Expand Down Expand Up @@ -87,6 +86,7 @@
" return df\n",
" \n",
"def ppo():\n",
" \n",
" \n",
"def ema(df: DataFrame):\n",
" '''\n",
Expand All @@ -109,7 +109,34 @@
" \n",
"def cmfi():\n",
"\n",
"def hma():\n",
"def hma(df):\n",
" '''\n",
" HMA: Hull Moving Average is a much faster and smoother moving average.\n",
" Computed over periods 6 to 20 (both inclusive)\n",
" \n",
" Arguement: \n",
" df: DataFrame object initialised from the csv data\n",
" Returns:\n",
" df: Modified DataFrame object\n",
" '''\n",
"\n",
" for len in range(6,21):\n",
" col_name = f'hma_{len}'\n",
" half_len = len//2\n",
" k = int(np.sqrt(len))\n",
"\n",
" half_weights = np.arange(1, half_len+1)\n",
" weights = np.arange(1, len+1)\n",
" \n",
" half_wma = df['Adj Close'].rolling(half_len).apply(lambda x: np.dot(x, half_weights)/half_weights.sum(), raw=True)\n",
" full_wma = df['Adj Close'].rolling(len).apply(lambda x: np.dot(x, weights)/weights.sum(), raw=True)\n",
"\n",
" diff = (2*half_wma) - full_wma\n",
"\n",
" w = np.arange(1, k+1)\n",
" df[col_name] = diff.rolling(k).apply(lambda x: np.dot(x, w)/w.sum(), raw=True)\n",
"\n",
" return df\n",
" \n",
"def dmi():\n",
" \n",
Expand All @@ -128,15 +155,15 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 40,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "unexpected EOF while parsing (<ipython-input-6-c7c1a64e3c53>, line 1)",
"output_type": "error",
"ename": "SyntaxError",
"evalue": "unexpected EOF while parsing (<ipython-input-40-c7c1a64e3c53>, line 1)",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-6-c7c1a64e3c53>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m def label_data():\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-40-c7c1a64e3c53>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m def label_data():\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
]
}
],
Expand Down Expand Up @@ -168,7 +195,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.6.9-final"
}
},
"nbformat": 4,
Expand Down