├── .ipynb_checkpoints
├── forex_stat_eurusd_m15_ohlc_binary_stat-checkpoint.ipynb
├── forex_stat_eurusd_m1_ohlc_binary_stat-checkpoint.ipynb
└── forex_stat_eurusd_m5_ohlc_binary_stat-checkpoint.ipynb
├── EURUSD_M15_202001020600_202012310000.csv
├── EURUSD_M1_202001020600_202012310000.csv
├── EURUSD_M5_202001020600_202012030000.csv
├── LICENSE
├── README.md
├── forex_stat_eurusd_m15_ohlc_binary_stat.ipynb
├── forex_stat_eurusd_m1_ohlc_binary_stat.ipynb
└── forex_stat_eurusd_m5_ohlc_binary_stat.ipynb
/.ipynb_checkpoints/forex_stat_eurusd_m1_ohlc_binary_stat-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Binary options put, call statistical research "
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "This Notebook analyse forex data from EURUSD pair M1 timeframe for the binary options market \n",
15 | "the data is from the duration of january 1st 2020 to december 31st 2020, the aim of this research is to find a pattern \n",
16 | "the placing trades in the binary options markert, in other to accurately predict where a market will close, from \n",
17 | "the previous candles, if it closes above the open price of the candle in question it is a call option, if it closes below it is a put option, when the open price and the close price is the same this is a no trade option which is statistically rare, so this won't be a focal point in this study"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 1,
23 | "metadata": {},
24 | "outputs": [],
25 | "source": [
26 | "#import the relevant packages\n",
27 | "import pandas as pd\n",
28 | "import numpy as np\n",
29 | "import seaborn as sns\n",
30 | "sns.set(color_codes=True)"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": 2,
36 | "metadata": {},
37 | "outputs": [
38 | {
39 | "data": {
40 | "text/html": [
41 | "
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42 | "\n",
55 | "
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56 | " \n",
57 | " \n",
58 | " \n",
59 | " <DATE> \n",
60 | " <TIME> \n",
61 | " <OPEN> \n",
62 | " <HIGH> \n",
63 | " <LOW> \n",
64 | " <CLOSE> \n",
65 | " <TICKVOL> \n",
66 | " <VOL> \n",
67 | " <SPREAD> \n",
68 | " \n",
69 | " \n",
70 | " \n",
71 | " \n",
72 | " 0 \n",
73 | " 2020.01.02 \n",
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141 | " ... \n",
142 | " \n",
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184 | " 1.22987 \n",
185 | " 1.22961 \n",
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188 | " 0 \n",
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190 | " \n",
191 | " \n",
192 | " 371715 \n",
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194 | " 00:00:00 \n",
195 | " 1.22963 \n",
196 | " 1.22978 \n",
197 | " 1.22963 \n",
198 | " 1.22975 \n",
199 | " 7 \n",
200 | " 0 \n",
201 | " 28 \n",
202 | " \n",
203 | " \n",
204 | "
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205 | "
371716 rows × 9 columns
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206 | "
"
207 | ],
208 | "text/plain": [
209 | " \\\n",
210 | "0 2020.01.02 06:00:00 1.12132 1.12133 1.12128 1.12131 14 \n",
211 | "1 2020.01.02 06:01:00 1.12131 1.12133 1.12131 1.12132 16 \n",
212 | "2 2020.01.02 06:02:00 1.12132 1.12133 1.12131 1.12132 9 \n",
213 | "3 2020.01.02 06:03:00 1.12132 1.12134 1.12132 1.12132 19 \n",
214 | "4 2020.01.02 06:04:00 1.12132 1.12133 1.12132 1.12132 3 \n",
215 | "... ... ... ... ... ... ... ... \n",
216 | "371711 2020.12.30 23:56:00 1.23003 1.23009 1.22994 1.23007 30 \n",
217 | "371712 2020.12.30 23:57:00 1.23007 1.23012 1.23003 1.23003 46 \n",
218 | "371713 2020.12.30 23:58:00 1.23004 1.23007 1.22997 1.22997 40 \n",
219 | "371714 2020.12.30 23:59:00 1.22987 1.22987 1.22961 1.22961 7 \n",
220 | "371715 2020.12.31 00:00:00 1.22963 1.22978 1.22963 1.22975 7 \n",
221 | "\n",
222 | " \n",
223 | "0 0 3 \n",
224 | "1 0 4 \n",
225 | "2 0 3 \n",
226 | "3 0 3 \n",
227 | "4 0 4 \n",
228 | "... ... ... \n",
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234 | "\n",
235 | "[371716 rows x 9 columns]"
236 | ]
237 | },
238 | "execution_count": 2,
239 | "metadata": {},
240 | "output_type": "execute_result"
241 | }
242 | ],
243 | "source": [
244 | "#read the data from a csv file\n",
245 | "data = pd.read_csv(\"EURUSD_M1_202001020600_202012310000.csv\", sep=\"\\t\")\n",
246 | "data"
247 | ]
248 | },
249 | {
250 | "cell_type": "code",
251 | "execution_count": 3,
252 | "metadata": {},
253 | "outputs": [],
254 | "source": [
255 | "#drop the , column as it is not needed in this research\n",
256 | "data.drop(\"\", inplace=True, axis=1)"
257 | ]
258 | },
259 | {
260 | "cell_type": "code",
261 | "execution_count": 4,
262 | "metadata": {},
263 | "outputs": [
264 | {
265 | "data": {
266 | "text/html": [
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287 | " <OPEN> \n",
288 | " <HIGH> \n",
289 | " <LOW> \n",
290 | " <CLOSE> \n",
291 | " <TICKVOL> \n",
292 | " <SPREAD> \n",
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419 | "
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420 | "
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422 | "text/plain": [
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424 | "0 2020.01.02 06:00:00 1.12132 1.12133 1.12128 1.12131 14 \n",
425 | "1 2020.01.02 06:01:00 1.12131 1.12133 1.12131 1.12132 16 \n",
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427 | "3 2020.01.02 06:03:00 1.12132 1.12134 1.12132 1.12132 19 \n",
428 | "4 2020.01.02 06:04:00 1.12132 1.12133 1.12132 1.12132 3 \n",
429 | "... ... ... ... ... ... ... ... \n",
430 | "371711 2020.12.30 23:56:00 1.23003 1.23009 1.22994 1.23007 30 \n",
431 | "371712 2020.12.30 23:57:00 1.23007 1.23012 1.23003 1.23003 46 \n",
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435 | "\n",
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449 | "[371716 rows x 8 columns]"
450 | ]
451 | },
452 | "execution_count": 4,
453 | "metadata": {},
454 | "output_type": "execute_result"
455 | }
456 | ],
457 | "source": [
458 | "data"
459 | ]
460 | },
461 | {
462 | "cell_type": "code",
463 | "execution_count": 5,
464 | "metadata": {},
465 | "outputs": [],
466 | "source": [
467 | "#set call when open price is less than close price\n",
468 | "data[\"\"] = data[\"\"] < data[\"\"]"
469 | ]
470 | },
471 | {
472 | "cell_type": "code",
473 | "execution_count": 6,
474 | "metadata": {},
475 | "outputs": [],
476 | "source": [
477 | "#set put when open price is greater than close price\n",
478 | "data[\"\"] = data[\"\"] > data[\"\"]"
479 | ]
480 | },
481 | {
482 | "cell_type": "code",
483 | "execution_count": 7,
484 | "metadata": {},
485 | "outputs": [],
486 | "source": [
487 | "#no trade when open price is equal to close price\n",
488 | "data[\"\"] = data[\"\"] == data[\"\"]"
489 | ]
490 | },
491 | {
492 | "cell_type": "code",
493 | "execution_count": 8,
494 | "metadata": {},
495 | "outputs": [
496 | {
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717 | "[371716 rows x 11 columns]"
718 | ]
719 | },
720 | "execution_count": 8,
721 | "metadata": {},
722 | "output_type": "execute_result"
723 | }
724 | ],
725 | "source": [
726 | "data"
727 | ]
728 | },
729 | {
730 | "cell_type": "code",
731 | "execution_count": 9,
732 | "metadata": {},
733 | "outputs": [],
734 | "source": [
735 | "#convert these columns to type int so they appear as one hot varibles\n",
736 | "data[[\"\", \"\", \"\"]] = data[[\"\", \"\", \"\"]].astype(\"int32\")"
737 | ]
738 | },
739 | {
740 | "cell_type": "code",
741 | "execution_count": 10,
742 | "metadata": {},
743 | "outputs": [
744 | {
745 | "data": {
746 | "text/html": [
747 | "\n",
748 | "\n",
761 | "
\n",
762 | " \n",
763 | " \n",
764 | " \n",
765 | " <DATE> \n",
766 | " <TIME> \n",
767 | " <OPEN> \n",
768 | " <HIGH> \n",
769 | " <LOW> \n",
770 | " <CLOSE> \n",
771 | " <TICKVOL> \n",
772 | " <SPREAD> \n",
773 | " <UP> \n",
774 | " <DOWN> \n",
775 | " <NO_MOVE> \n",
776 | " \n",
777 | " \n",
778 | " \n",
779 | " \n",
780 | " 0 \n",
781 | " 2020.01.02 \n",
782 | " 06:00:00 \n",
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830 | " 3 \n",
831 | " 0 \n",
832 | " 0 \n",
833 | " 1 \n",
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835 | " \n",
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837 | " 2020.01.02 \n",
838 | " 06:04:00 \n",
839 | " 1.12132 \n",
840 | " 1.12133 \n",
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842 | " 1.12132 \n",
843 | " 3 \n",
844 | " 4 \n",
845 | " 0 \n",
846 | " 0 \n",
847 | " 1 \n",
848 | " \n",
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851 | " ... \n",
852 | " ... \n",
853 | " ... \n",
854 | " ... \n",
855 | " ... \n",
856 | " ... \n",
857 | " ... \n",
858 | " ... \n",
859 | " ... \n",
860 | " ... \n",
861 | " ... \n",
862 | " \n",
863 | " \n",
864 | " 371711 \n",
865 | " 2020.12.30 \n",
866 | " 23:56:00 \n",
867 | " 1.23003 \n",
868 | " 1.23009 \n",
869 | " 1.22994 \n",
870 | " 1.23007 \n",
871 | " 30 \n",
872 | " 0 \n",
873 | " 1 \n",
874 | " 0 \n",
875 | " 0 \n",
876 | " \n",
877 | " \n",
878 | " 371712 \n",
879 | " 2020.12.30 \n",
880 | " 23:57:00 \n",
881 | " 1.23007 \n",
882 | " 1.23012 \n",
883 | " 1.23003 \n",
884 | " 1.23003 \n",
885 | " 46 \n",
886 | " 0 \n",
887 | " 0 \n",
888 | " 1 \n",
889 | " 0 \n",
890 | " \n",
891 | " \n",
892 | " 371713 \n",
893 | " 2020.12.30 \n",
894 | " 23:58:00 \n",
895 | " 1.23004 \n",
896 | " 1.23007 \n",
897 | " 1.22997 \n",
898 | " 1.22997 \n",
899 | " 40 \n",
900 | " 0 \n",
901 | " 0 \n",
902 | " 1 \n",
903 | " 0 \n",
904 | " \n",
905 | " \n",
906 | " 371714 \n",
907 | " 2020.12.30 \n",
908 | " 23:59:00 \n",
909 | " 1.22987 \n",
910 | " 1.22987 \n",
911 | " 1.22961 \n",
912 | " 1.22961 \n",
913 | " 7 \n",
914 | " 10 \n",
915 | " 0 \n",
916 | " 1 \n",
917 | " 0 \n",
918 | " \n",
919 | " \n",
920 | " 371715 \n",
921 | " 2020.12.31 \n",
922 | " 00:00:00 \n",
923 | " 1.22963 \n",
924 | " 1.22978 \n",
925 | " 1.22963 \n",
926 | " 1.22975 \n",
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929 | " 1 \n",
930 | " 0 \n",
931 | " 0 \n",
932 | " \n",
933 | " \n",
934 | "
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935 | "
371716 rows × 11 columns
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936 | "
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937 | ],
938 | "text/plain": [
939 | " \\\n",
940 | "0 2020.01.02 06:00:00 1.12132 1.12133 1.12128 1.12131 14 \n",
941 | "1 2020.01.02 06:01:00 1.12131 1.12133 1.12131 1.12132 16 \n",
942 | "2 2020.01.02 06:02:00 1.12132 1.12133 1.12131 1.12132 9 \n",
943 | "3 2020.01.02 06:03:00 1.12132 1.12134 1.12132 1.12132 19 \n",
944 | "4 2020.01.02 06:04:00 1.12132 1.12133 1.12132 1.12132 3 \n",
945 | "... ... ... ... ... ... ... ... \n",
946 | "371711 2020.12.30 23:56:00 1.23003 1.23009 1.22994 1.23007 30 \n",
947 | "371712 2020.12.30 23:57:00 1.23007 1.23012 1.23003 1.23003 46 \n",
948 | "371713 2020.12.30 23:58:00 1.23004 1.23007 1.22997 1.22997 40 \n",
949 | "371714 2020.12.30 23:59:00 1.22987 1.22987 1.22961 1.22961 7 \n",
950 | "371715 2020.12.31 00:00:00 1.22963 1.22978 1.22963 1.22975 7 \n",
951 | "\n",
952 | " \n",
953 | "0 3 0 1 0 \n",
954 | "1 4 1 0 0 \n",
955 | "2 3 0 0 1 \n",
956 | "3 3 0 0 1 \n",
957 | "4 4 0 0 1 \n",
958 | "... ... ... ... ... \n",
959 | "371711 0 1 0 0 \n",
960 | "371712 0 0 1 0 \n",
961 | "371713 0 0 1 0 \n",
962 | "371714 10 0 1 0 \n",
963 | "371715 28 1 0 0 \n",
964 | "\n",
965 | "[371716 rows x 11 columns]"
966 | ]
967 | },
968 | "execution_count": 10,
969 | "metadata": {},
970 | "output_type": "execute_result"
971 | }
972 | ],
973 | "source": [
974 | "data"
975 | ]
976 | },
977 | {
978 | "cell_type": "code",
979 | "execution_count": 11,
980 | "metadata": {},
981 | "outputs": [
982 | {
983 | "data": {
984 | "text/plain": [
985 | "0 1.0\n",
986 | "1 -1.0\n",
987 | "2 0.0\n",
988 | "3 0.0\n",
989 | "4 0.0\n",
990 | " ... \n",
991 | "371711 -4.0\n",
992 | "371712 4.0\n",
993 | "371713 7.0\n",
994 | "371714 26.0\n",
995 | "371715 -12.0\n",
996 | "Length: 371716, dtype: float64"
997 | ]
998 | },
999 | "execution_count": 11,
1000 | "metadata": {},
1001 | "output_type": "execute_result"
1002 | }
1003 | ],
1004 | "source": [
1005 | "(data[\"\"] - data[\"\"]) * 100000"
1006 | ]
1007 | },
1008 | {
1009 | "cell_type": "raw",
1010 | "metadata": {},
1011 | "source": [
1012 | "The next cell is used to get pippette value by subracting two columns\n",
1013 | "from each other and multiplying them by 100000 to get the pipette value,\n",
1014 | "the columns to be subracted includes:\n",
1015 | "\n",
1016 | "open - close = open_close\n",
1017 | "open - low = open_low\n",
1018 | "open - high = open_high\n",
1019 | "close - low = close_low\n",
1020 | "close - high = close_high\n",
1021 | "high - low = high_low"
1022 | ]
1023 | },
1024 | {
1025 | "cell_type": "code",
1026 | "execution_count": 12,
1027 | "metadata": {},
1028 | "outputs": [],
1029 | "source": [
1030 | "open_close = (data[\"\"] - data[\"\"]) * 100000\n",
1031 | "open_low = (data[\"\"] - data[\"\"]) * 100000\n",
1032 | "open_high = (data[\"\"] - data[\"\"]) * 100000\n",
1033 | "close_low = (data[\"\"] - data[\"\"]) * 100000\n",
1034 | "close_high = (data[\"\"] - data[\"\"]) * 100000\n",
1035 | "high_low = (data[\"\"] - data[\"\"]) * 100000"
1036 | ]
1037 | },
1038 | {
1039 | "cell_type": "code",
1040 | "execution_count": 13,
1041 | "metadata": {},
1042 | "outputs": [
1043 | {
1044 | "data": {
1045 | "text/html": [
1046 | "\n",
1047 | "\n",
1060 | "
\n",
1061 | " \n",
1062 | " \n",
1063 | " \n",
1064 | " open_close \n",
1065 | " open_low \n",
1066 | " open_high \n",
1067 | " close_low \n",
1068 | " close_high \n",
1069 | " high_low \n",
1070 | " \n",
1071 | " \n",
1072 | " \n",
1073 | " \n",
1074 | " 0 \n",
1075 | " 1.0 \n",
1076 | " 4.0 \n",
1077 | " -1.0 \n",
1078 | " 3.0 \n",
1079 | " -2.0 \n",
1080 | " 5.0 \n",
1081 | " \n",
1082 | " \n",
1083 | " 1 \n",
1084 | " -1.0 \n",
1085 | " 0.0 \n",
1086 | " -2.0 \n",
1087 | " 1.0 \n",
1088 | " -1.0 \n",
1089 | " 2.0 \n",
1090 | " \n",
1091 | " \n",
1092 | " 2 \n",
1093 | " 0.0 \n",
1094 | " 1.0 \n",
1095 | " -1.0 \n",
1096 | " 1.0 \n",
1097 | " -1.0 \n",
1098 | " 2.0 \n",
1099 | " \n",
1100 | " \n",
1101 | " 3 \n",
1102 | " 0.0 \n",
1103 | " 0.0 \n",
1104 | " -2.0 \n",
1105 | " 0.0 \n",
1106 | " -2.0 \n",
1107 | " 2.0 \n",
1108 | " \n",
1109 | " \n",
1110 | " 4 \n",
1111 | " 0.0 \n",
1112 | " 0.0 \n",
1113 | " -1.0 \n",
1114 | " 0.0 \n",
1115 | " -1.0 \n",
1116 | " 1.0 \n",
1117 | " \n",
1118 | " \n",
1119 | " ... \n",
1120 | " ... \n",
1121 | " ... \n",
1122 | " ... \n",
1123 | " ... \n",
1124 | " ... \n",
1125 | " ... \n",
1126 | " \n",
1127 | " \n",
1128 | " 371711 \n",
1129 | " -4.0 \n",
1130 | " 9.0 \n",
1131 | " -6.0 \n",
1132 | " 13.0 \n",
1133 | " -2.0 \n",
1134 | " 15.0 \n",
1135 | " \n",
1136 | " \n",
1137 | " 371712 \n",
1138 | " 4.0 \n",
1139 | " 4.0 \n",
1140 | " -5.0 \n",
1141 | " 0.0 \n",
1142 | " -9.0 \n",
1143 | " 9.0 \n",
1144 | " \n",
1145 | " \n",
1146 | " 371713 \n",
1147 | " 7.0 \n",
1148 | " 7.0 \n",
1149 | " -3.0 \n",
1150 | " 0.0 \n",
1151 | " -10.0 \n",
1152 | " 10.0 \n",
1153 | " \n",
1154 | " \n",
1155 | " 371714 \n",
1156 | " 26.0 \n",
1157 | " 26.0 \n",
1158 | " 0.0 \n",
1159 | " 0.0 \n",
1160 | " -26.0 \n",
1161 | " 26.0 \n",
1162 | " \n",
1163 | " \n",
1164 | " 371715 \n",
1165 | " -12.0 \n",
1166 | " 0.0 \n",
1167 | " -15.0 \n",
1168 | " 12.0 \n",
1169 | " -3.0 \n",
1170 | " 15.0 \n",
1171 | " \n",
1172 | " \n",
1173 | "
\n",
1174 | "
371716 rows × 6 columns
\n",
1175 | "
"
1176 | ],
1177 | "text/plain": [
1178 | " open_close open_low open_high close_low close_high high_low\n",
1179 | "0 1.0 4.0 -1.0 3.0 -2.0 5.0\n",
1180 | "1 -1.0 0.0 -2.0 1.0 -1.0 2.0\n",
1181 | "2 0.0 1.0 -1.0 1.0 -1.0 2.0\n",
1182 | "3 0.0 0.0 -2.0 0.0 -2.0 2.0\n",
1183 | "4 0.0 0.0 -1.0 0.0 -1.0 1.0\n",
1184 | "... ... ... ... ... ... ...\n",
1185 | "371711 -4.0 9.0 -6.0 13.0 -2.0 15.0\n",
1186 | "371712 4.0 4.0 -5.0 0.0 -9.0 9.0\n",
1187 | "371713 7.0 7.0 -3.0 0.0 -10.0 10.0\n",
1188 | "371714 26.0 26.0 0.0 0.0 -26.0 26.0\n",
1189 | "371715 -12.0 0.0 -15.0 12.0 -3.0 15.0\n",
1190 | "\n",
1191 | "[371716 rows x 6 columns]"
1192 | ]
1193 | },
1194 | "execution_count": 13,
1195 | "metadata": {},
1196 | "output_type": "execute_result"
1197 | }
1198 | ],
1199 | "source": [
1200 | "#create a dataframe of the subtracted columns\n",
1201 | "move_df = pd.DataFrame({\"open_close\" : open_close, \n",
1202 | " \"open_low\" : open_low,\n",
1203 | " \"open_high\" : open_high,\n",
1204 | " \"close_low\" : close_low,\n",
1205 | " \"close_high\" : close_high,\n",
1206 | " \"high_low\" : high_low, })\n",
1207 | "move_df"
1208 | ]
1209 | },
1210 | {
1211 | "cell_type": "markdown",
1212 | "metadata": {},
1213 | "source": [
1214 | "### create a future dataframe to store the next data i.e the candle in question "
1215 | ]
1216 | },
1217 | {
1218 | "cell_type": "code",
1219 | "execution_count": 14,
1220 | "metadata": {},
1221 | "outputs": [],
1222 | "source": [
1223 | "future = data.iloc[1:,8:11]"
1224 | ]
1225 | },
1226 | {
1227 | "cell_type": "code",
1228 | "execution_count": 15,
1229 | "metadata": {},
1230 | "outputs": [],
1231 | "source": [
1232 | "future[\"open*\"] = data.iloc[1:,2]"
1233 | ]
1234 | },
1235 | {
1236 | "cell_type": "code",
1237 | "execution_count": 16,
1238 | "metadata": {},
1239 | "outputs": [],
1240 | "source": [
1241 | "future.columns = [\"up*\", \"down*\", \"no_move*\", \"open*\"]"
1242 | ]
1243 | },
1244 | {
1245 | "cell_type": "code",
1246 | "execution_count": 17,
1247 | "metadata": {},
1248 | "outputs": [],
1249 | "source": [
1250 | "future.reset_index(inplace=True)\n",
1251 | "future.drop([\"index\"], axis = 1, inplace=True)"
1252 | ]
1253 | },
1254 | {
1255 | "cell_type": "code",
1256 | "execution_count": 18,
1257 | "metadata": {},
1258 | "outputs": [
1259 | {
1260 | "data": {
1261 | "text/html": [
1262 | "\n",
1263 | "\n",
1276 | "
\n",
1277 | " \n",
1278 | " \n",
1279 | " \n",
1280 | " open_close \n",
1281 | " open_low \n",
1282 | " open_high \n",
1283 | " close_low \n",
1284 | " close_high \n",
1285 | " high_low \n",
1286 | " up* \n",
1287 | " down* \n",
1288 | " no_move* \n",
1289 | " open* \n",
1290 | " \n",
1291 | " \n",
1292 | " \n",
1293 | " \n",
1294 | " 0 \n",
1295 | " 1.0 \n",
1296 | " 4.0 \n",
1297 | " -1.0 \n",
1298 | " 3.0 \n",
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1300 | " 5.0 \n",
1301 | " 1.0 \n",
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1320 | " 2 \n",
1321 | " 0.0 \n",
1322 | " 1.0 \n",
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1325 | " -1.0 \n",
1326 | " 2.0 \n",
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1328 | " 0.0 \n",
1329 | " 1.0 \n",
1330 | " 1.12132 \n",
1331 | " \n",
1332 | " \n",
1333 | " 3 \n",
1334 | " 0.0 \n",
1335 | " 0.0 \n",
1336 | " -2.0 \n",
1337 | " 0.0 \n",
1338 | " -2.0 \n",
1339 | " 2.0 \n",
1340 | " 0.0 \n",
1341 | " 0.0 \n",
1342 | " 1.0 \n",
1343 | " 1.12132 \n",
1344 | " \n",
1345 | " \n",
1346 | " 4 \n",
1347 | " 0.0 \n",
1348 | " 0.0 \n",
1349 | " -1.0 \n",
1350 | " 0.0 \n",
1351 | " -1.0 \n",
1352 | " 1.0 \n",
1353 | " 0.0 \n",
1354 | " 1.0 \n",
1355 | " 0.0 \n",
1356 | " 1.12132 \n",
1357 | " \n",
1358 | " \n",
1359 | " ... \n",
1360 | " ... \n",
1361 | " ... \n",
1362 | " ... \n",
1363 | " ... \n",
1364 | " ... \n",
1365 | " ... \n",
1366 | " ... \n",
1367 | " ... \n",
1368 | " ... \n",
1369 | " ... \n",
1370 | " \n",
1371 | " \n",
1372 | " 371711 \n",
1373 | " -4.0 \n",
1374 | " 9.0 \n",
1375 | " -6.0 \n",
1376 | " 13.0 \n",
1377 | " -2.0 \n",
1378 | " 15.0 \n",
1379 | " 0.0 \n",
1380 | " 1.0 \n",
1381 | " 0.0 \n",
1382 | " 1.23007 \n",
1383 | " \n",
1384 | " \n",
1385 | " 371712 \n",
1386 | " 4.0 \n",
1387 | " 4.0 \n",
1388 | " -5.0 \n",
1389 | " 0.0 \n",
1390 | " -9.0 \n",
1391 | " 9.0 \n",
1392 | " 0.0 \n",
1393 | " 1.0 \n",
1394 | " 0.0 \n",
1395 | " 1.23004 \n",
1396 | " \n",
1397 | " \n",
1398 | " 371713 \n",
1399 | " 7.0 \n",
1400 | " 7.0 \n",
1401 | " -3.0 \n",
1402 | " 0.0 \n",
1403 | " -10.0 \n",
1404 | " 10.0 \n",
1405 | " 0.0 \n",
1406 | " 1.0 \n",
1407 | " 0.0 \n",
1408 | " 1.22987 \n",
1409 | " \n",
1410 | " \n",
1411 | " 371714 \n",
1412 | " 26.0 \n",
1413 | " 26.0 \n",
1414 | " 0.0 \n",
1415 | " 0.0 \n",
1416 | " -26.0 \n",
1417 | " 26.0 \n",
1418 | " 1.0 \n",
1419 | " 0.0 \n",
1420 | " 0.0 \n",
1421 | " 1.22963 \n",
1422 | " \n",
1423 | " \n",
1424 | " 371715 \n",
1425 | " -12.0 \n",
1426 | " 0.0 \n",
1427 | " -15.0 \n",
1428 | " 12.0 \n",
1429 | " -3.0 \n",
1430 | " 15.0 \n",
1431 | " NaN \n",
1432 | " NaN \n",
1433 | " NaN \n",
1434 | " NaN \n",
1435 | " \n",
1436 | " \n",
1437 | "
\n",
1438 | "
371716 rows × 10 columns
\n",
1439 | "
"
1440 | ],
1441 | "text/plain": [
1442 | " open_close open_low open_high close_low close_high high_low up* \\\n",
1443 | "0 1.0 4.0 -1.0 3.0 -2.0 5.0 1.0 \n",
1444 | "1 -1.0 0.0 -2.0 1.0 -1.0 2.0 0.0 \n",
1445 | "2 0.0 1.0 -1.0 1.0 -1.0 2.0 0.0 \n",
1446 | "3 0.0 0.0 -2.0 0.0 -2.0 2.0 0.0 \n",
1447 | "4 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 \n",
1448 | "... ... ... ... ... ... ... ... \n",
1449 | "371711 -4.0 9.0 -6.0 13.0 -2.0 15.0 0.0 \n",
1450 | "371712 4.0 4.0 -5.0 0.0 -9.0 9.0 0.0 \n",
1451 | "371713 7.0 7.0 -3.0 0.0 -10.0 10.0 0.0 \n",
1452 | "371714 26.0 26.0 0.0 0.0 -26.0 26.0 1.0 \n",
1453 | "371715 -12.0 0.0 -15.0 12.0 -3.0 15.0 NaN \n",
1454 | "\n",
1455 | " down* no_move* open* \n",
1456 | "0 0.0 0.0 1.12131 \n",
1457 | "1 0.0 1.0 1.12132 \n",
1458 | "2 0.0 1.0 1.12132 \n",
1459 | "3 0.0 1.0 1.12132 \n",
1460 | "4 1.0 0.0 1.12132 \n",
1461 | "... ... ... ... \n",
1462 | "371711 1.0 0.0 1.23007 \n",
1463 | "371712 1.0 0.0 1.23004 \n",
1464 | "371713 1.0 0.0 1.22987 \n",
1465 | "371714 0.0 0.0 1.22963 \n",
1466 | "371715 NaN NaN NaN \n",
1467 | "\n",
1468 | "[371716 rows x 10 columns]"
1469 | ]
1470 | },
1471 | "execution_count": 18,
1472 | "metadata": {},
1473 | "output_type": "execute_result"
1474 | }
1475 | ],
1476 | "source": [
1477 | "#concat move_df and future to create a new dataframe\n",
1478 | "dataset = pd.concat([move_df, future], axis = 1)\n",
1479 | "dataset"
1480 | ]
1481 | },
1482 | {
1483 | "cell_type": "code",
1484 | "execution_count": 19,
1485 | "metadata": {},
1486 | "outputs": [],
1487 | "source": [
1488 | "dataset.dropna(inplace=True)"
1489 | ]
1490 | },
1491 | {
1492 | "cell_type": "code",
1493 | "execution_count": 20,
1494 | "metadata": {},
1495 | "outputs": [
1496 | {
1497 | "data": {
1498 | "text/html": [
1499 | "\n",
1500 | "\n",
1513 | "
\n",
1514 | " \n",
1515 | " \n",
1516 | " \n",
1517 | " open_close \n",
1518 | " open_low \n",
1519 | " open_high \n",
1520 | " close_low \n",
1521 | " close_high \n",
1522 | " high_low \n",
1523 | " up* \n",
1524 | " down* \n",
1525 | " no_move* \n",
1526 | " open* \n",
1527 | " \n",
1528 | " \n",
1529 | " \n",
1530 | " \n",
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1532 | " 1.0 \n",
1533 | " 4.0 \n",
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1576 | " 2.0 \n",
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1578 | " 0.0 \n",
1579 | " 1.0 \n",
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1581 | " \n",
1582 | " \n",
1583 | " 4 \n",
1584 | " 0.0 \n",
1585 | " 0.0 \n",
1586 | " -1.0 \n",
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1588 | " -1.0 \n",
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1593 | " 1.12132 \n",
1594 | " \n",
1595 | " \n",
1596 | " ... \n",
1597 | " ... \n",
1598 | " ... \n",
1599 | " ... \n",
1600 | " ... \n",
1601 | " ... \n",
1602 | " ... \n",
1603 | " ... \n",
1604 | " ... \n",
1605 | " ... \n",
1606 | " ... \n",
1607 | " \n",
1608 | " \n",
1609 | " 371710 \n",
1610 | " -9.0 \n",
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1612 | " -17.0 \n",
1613 | " 10.0 \n",
1614 | " -8.0 \n",
1615 | " 18.0 \n",
1616 | " 1.0 \n",
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1619 | " 1.23003 \n",
1620 | " \n",
1621 | " \n",
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1623 | " -4.0 \n",
1624 | " 9.0 \n",
1625 | " -6.0 \n",
1626 | " 13.0 \n",
1627 | " -2.0 \n",
1628 | " 15.0 \n",
1629 | " 0.0 \n",
1630 | " 1.0 \n",
1631 | " 0.0 \n",
1632 | " 1.23007 \n",
1633 | " \n",
1634 | " \n",
1635 | " 371712 \n",
1636 | " 4.0 \n",
1637 | " 4.0 \n",
1638 | " -5.0 \n",
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1640 | " -9.0 \n",
1641 | " 9.0 \n",
1642 | " 0.0 \n",
1643 | " 1.0 \n",
1644 | " 0.0 \n",
1645 | " 1.23004 \n",
1646 | " \n",
1647 | " \n",
1648 | " 371713 \n",
1649 | " 7.0 \n",
1650 | " 7.0 \n",
1651 | " -3.0 \n",
1652 | " 0.0 \n",
1653 | " -10.0 \n",
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1656 | " 1.0 \n",
1657 | " 0.0 \n",
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1659 | " \n",
1660 | " \n",
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1671 | " 1.22963 \n",
1672 | " \n",
1673 | " \n",
1674 | "
\n",
1675 | "
371715 rows × 10 columns
\n",
1676 | "
"
1677 | ],
1678 | "text/plain": [
1679 | " open_close open_low open_high close_low close_high high_low up* \\\n",
1680 | "0 1.0 4.0 -1.0 3.0 -2.0 5.0 1.0 \n",
1681 | "1 -1.0 0.0 -2.0 1.0 -1.0 2.0 0.0 \n",
1682 | "2 0.0 1.0 -1.0 1.0 -1.0 2.0 0.0 \n",
1683 | "3 0.0 0.0 -2.0 0.0 -2.0 2.0 0.0 \n",
1684 | "4 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 \n",
1685 | "... ... ... ... ... ... ... ... \n",
1686 | "371710 -9.0 1.0 -17.0 10.0 -8.0 18.0 1.0 \n",
1687 | "371711 -4.0 9.0 -6.0 13.0 -2.0 15.0 0.0 \n",
1688 | "371712 4.0 4.0 -5.0 0.0 -9.0 9.0 0.0 \n",
1689 | "371713 7.0 7.0 -3.0 0.0 -10.0 10.0 0.0 \n",
1690 | "371714 26.0 26.0 0.0 0.0 -26.0 26.0 1.0 \n",
1691 | "\n",
1692 | " down* no_move* open* \n",
1693 | "0 0.0 0.0 1.12131 \n",
1694 | "1 0.0 1.0 1.12132 \n",
1695 | "2 0.0 1.0 1.12132 \n",
1696 | "3 0.0 1.0 1.12132 \n",
1697 | "4 1.0 0.0 1.12132 \n",
1698 | "... ... ... ... \n",
1699 | "371710 0.0 0.0 1.23003 \n",
1700 | "371711 1.0 0.0 1.23007 \n",
1701 | "371712 1.0 0.0 1.23004 \n",
1702 | "371713 1.0 0.0 1.22987 \n",
1703 | "371714 0.0 0.0 1.22963 \n",
1704 | "\n",
1705 | "[371715 rows x 10 columns]"
1706 | ]
1707 | },
1708 | "execution_count": 20,
1709 | "metadata": {},
1710 | "output_type": "execute_result"
1711 | }
1712 | ],
1713 | "source": [
1714 | "dataset"
1715 | ]
1716 | },
1717 | {
1718 | "cell_type": "code",
1719 | "execution_count": 38,
1720 | "metadata": {},
1721 | "outputs": [
1722 | {
1723 | "data": {
1724 | "text/plain": [
1725 | "up* down* no_move*\n",
1726 | "1.0 0.0 0.0 173066\n",
1727 | "0.0 1.0 0.0 170054\n",
1728 | " 0.0 1.0 28595\n",
1729 | "dtype: int64"
1730 | ]
1731 | },
1732 | "execution_count": 38,
1733 | "metadata": {},
1734 | "output_type": "execute_result"
1735 | }
1736 | ],
1737 | "source": [
1738 | "#display frequencies of up, down and no move\n",
1739 | "dataset[[\"up*\", \"down*\", \"no_move*\"]].value_counts()"
1740 | ]
1741 | },
1742 | {
1743 | "cell_type": "markdown",
1744 | "metadata": {},
1745 | "source": [
1746 | "### Anaylyse candles after a prior large drop in prices up to 15 pips in a minute"
1747 | ]
1748 | },
1749 | {
1750 | "cell_type": "markdown",
1751 | "metadata": {},
1752 | "source": [
1753 | "we are trying to know the time of move that will follow a big drop in price by 15 pips"
1754 | ]
1755 | },
1756 | {
1757 | "cell_type": "code",
1758 | "execution_count": 21,
1759 | "metadata": {},
1760 | "outputs": [
1761 | {
1762 | "data": {
1763 | "text/html": [
1764 | "\n",
1765 | "\n",
1778 | "
\n",
1779 | " \n",
1780 | " \n",
1781 | " \n",
1782 | " open_close \n",
1783 | " open_low \n",
1784 | " open_high \n",
1785 | " close_low \n",
1786 | " close_high \n",
1787 | " high_low \n",
1788 | " up* \n",
1789 | " down* \n",
1790 | " no_move* \n",
1791 | " open* \n",
1792 | " \n",
1793 | " \n",
1794 | " \n",
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1796 | " 62514 \n",
1797 | " 225.0 \n",
1798 | " 230.0 \n",
1799 | " -6.0 \n",
1800 | " 5.0 \n",
1801 | " -231.0 \n",
1802 | " 236.0 \n",
1803 | " 1.0 \n",
1804 | " 0.0 \n",
1805 | " 0.0 \n",
1806 | " 1.11575 \n",
1807 | " \n",
1808 | " \n",
1809 | " 63952 \n",
1810 | " 170.0 \n",
1811 | " 174.0 \n",
1812 | " -46.0 \n",
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1814 | " -216.0 \n",
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1818 | " 0.0 \n",
1819 | " 1.11011 \n",
1820 | " \n",
1821 | " \n",
1822 | " 66858 \n",
1823 | " 200.0 \n",
1824 | " 200.0 \n",
1825 | " 0.0 \n",
1826 | " 0.0 \n",
1827 | " -200.0 \n",
1828 | " 200.0 \n",
1829 | " 1.0 \n",
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1831 | " 0.0 \n",
1832 | " 1.12902 \n",
1833 | " \n",
1834 | " \n",
1835 | " 67476 \n",
1836 | " 229.0 \n",
1837 | " 265.0 \n",
1838 | " -305.0 \n",
1839 | " 36.0 \n",
1840 | " -534.0 \n",
1841 | " 570.0 \n",
1842 | " 0.0 \n",
1843 | " 1.0 \n",
1844 | " 0.0 \n",
1845 | " 1.14440 \n",
1846 | " \n",
1847 | " \n",
1848 | " 67480 \n",
1849 | " 200.0 \n",
1850 | " 221.0 \n",
1851 | " -14.0 \n",
1852 | " 21.0 \n",
1853 | " -214.0 \n",
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1855 | " 1.0 \n",
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1857 | " 0.0 \n",
1858 | " 1.14345 \n",
1859 | " \n",
1860 | " \n",
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1862 | " 159.0 \n",
1863 | " 160.0 \n",
1864 | " -2.0 \n",
1865 | " 1.0 \n",
1866 | " -161.0 \n",
1867 | " 162.0 \n",
1868 | " 0.0 \n",
1869 | " 1.0 \n",
1870 | " 0.0 \n",
1871 | " 1.14619 \n",
1872 | " \n",
1873 | " \n",
1874 | " 68192 \n",
1875 | " 349.0 \n",
1876 | " 455.0 \n",
1877 | " -6.0 \n",
1878 | " 106.0 \n",
1879 | " -355.0 \n",
1880 | " 461.0 \n",
1881 | " 1.0 \n",
1882 | " 0.0 \n",
1883 | " 0.0 \n",
1884 | " 1.14162 \n",
1885 | " \n",
1886 | " \n",
1887 | " 72453 \n",
1888 | " 374.0 \n",
1889 | " 382.0 \n",
1890 | " 0.0 \n",
1891 | " 8.0 \n",
1892 | " -374.0 \n",
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1895 | " 0.0 \n",
1896 | " 0.0 \n",
1897 | " 1.12120 \n",
1898 | " \n",
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1902 | " 184.0 \n",
1903 | " 0.0 \n",
1904 | " 0.0 \n",
1905 | " -184.0 \n",
1906 | " 184.0 \n",
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1908 | " 0.0 \n",
1909 | " 0.0 \n",
1910 | " 1.12207 \n",
1911 | " \n",
1912 | " \n",
1913 | " 72522 \n",
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1915 | " 176.0 \n",
1916 | " 0.0 \n",
1917 | " 6.0 \n",
1918 | " -170.0 \n",
1919 | " 176.0 \n",
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1921 | " 1.0 \n",
1922 | " 0.0 \n",
1923 | " 1.11985 \n",
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1925 | " \n",
1926 | " 72523 \n",
1927 | " 190.0 \n",
1928 | " 237.0 \n",
1929 | " 0.0 \n",
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1931 | " -190.0 \n",
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1935 | " 0.0 \n",
1936 | " 1.11795 \n",
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2000 | " 0.0 \n",
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2002 | " \n",
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2005 | " 156.0 \n",
2006 | " 159.0 \n",
2007 | " 0.0 \n",
2008 | " 3.0 \n",
2009 | " -156.0 \n",
2010 | " 159.0 \n",
2011 | " 0.0 \n",
2012 | " 1.0 \n",
2013 | " 0.0 \n",
2014 | " 1.10942 \n",
2015 | " \n",
2016 | " \n",
2017 | " 75769 \n",
2018 | " 153.0 \n",
2019 | " 196.0 \n",
2020 | " -4.0 \n",
2021 | " 43.0 \n",
2022 | " -157.0 \n",
2023 | " 200.0 \n",
2024 | " 1.0 \n",
2025 | " 0.0 \n",
2026 | " 0.0 \n",
2027 | " 1.11639 \n",
2028 | " \n",
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2030 | " 78759 \n",
2031 | " 153.0 \n",
2032 | " 153.0 \n",
2033 | " -1.0 \n",
2034 | " 0.0 \n",
2035 | " -154.0 \n",
2036 | " 154.0 \n",
2037 | " 0.0 \n",
2038 | " 1.0 \n",
2039 | " 0.0 \n",
2040 | " 1.09474 \n",
2041 | " \n",
2042 | " \n",
2043 | " 79263 \n",
2044 | " 161.0 \n",
2045 | " 175.0 \n",
2046 | " -10.0 \n",
2047 | " 14.0 \n",
2048 | " -171.0 \n",
2049 | " 185.0 \n",
2050 | " 1.0 \n",
2051 | " 0.0 \n",
2052 | " 0.0 \n",
2053 | " 1.08754 \n",
2054 | " \n",
2055 | " \n",
2056 | " 79271 \n",
2057 | " 154.0 \n",
2058 | " 171.0 \n",
2059 | " -12.0 \n",
2060 | " 17.0 \n",
2061 | " -166.0 \n",
2062 | " 183.0 \n",
2063 | " 0.0 \n",
2064 | " 1.0 \n",
2065 | " 0.0 \n",
2066 | " 1.08629 \n",
2067 | " \n",
2068 | " \n",
2069 | " 79301 \n",
2070 | " 193.0 \n",
2071 | " 197.0 \n",
2072 | " -9.0 \n",
2073 | " 4.0 \n",
2074 | " -202.0 \n",
2075 | " 206.0 \n",
2076 | " 0.0 \n",
2077 | " 1.0 \n",
2078 | " 0.0 \n",
2079 | " 1.08295 \n",
2080 | " \n",
2081 | " \n",
2082 | " 79505 \n",
2083 | " 175.0 \n",
2084 | " 177.0 \n",
2085 | " 0.0 \n",
2086 | " 2.0 \n",
2087 | " -175.0 \n",
2088 | " 177.0 \n",
2089 | " 0.0 \n",
2090 | " 1.0 \n",
2091 | " 0.0 \n",
2092 | " 1.07425 \n",
2093 | " \n",
2094 | " \n",
2095 | " 79522 \n",
2096 | " 176.0 \n",
2097 | " 176.0 \n",
2098 | " -5.0 \n",
2099 | " 0.0 \n",
2100 | " -181.0 \n",
2101 | " 181.0 \n",
2102 | " 1.0 \n",
2103 | " 0.0 \n",
2104 | " 0.0 \n",
2105 | " 1.07424 \n",
2106 | " \n",
2107 | " \n",
2108 | " 79590 \n",
2109 | " 162.0 \n",
2110 | " 195.0 \n",
2111 | " -3.0 \n",
2112 | " 33.0 \n",
2113 | " -165.0 \n",
2114 | " 198.0 \n",
2115 | " 0.0 \n",
2116 | " 1.0 \n",
2117 | " 0.0 \n",
2118 | " 1.07986 \n",
2119 | " \n",
2120 | " \n",
2121 | " 79771 \n",
2122 | " 173.0 \n",
2123 | " 181.0 \n",
2124 | " 0.0 \n",
2125 | " 8.0 \n",
2126 | " -173.0 \n",
2127 | " 181.0 \n",
2128 | " 0.0 \n",
2129 | " 1.0 \n",
2130 | " 0.0 \n",
2131 | " 1.07055 \n",
2132 | " \n",
2133 | " \n",
2134 | " 79800 \n",
2135 | " 207.0 \n",
2136 | " 210.0 \n",
2137 | " -11.0 \n",
2138 | " 3.0 \n",
2139 | " -218.0 \n",
2140 | " 221.0 \n",
2141 | " 1.0 \n",
2142 | " 0.0 \n",
2143 | " 0.0 \n",
2144 | " 1.06826 \n",
2145 | " \n",
2146 | " \n",
2147 | " 82380 \n",
2148 | " 195.0 \n",
2149 | " 203.0 \n",
2150 | " -6.0 \n",
2151 | " 8.0 \n",
2152 | " -201.0 \n",
2153 | " 209.0 \n",
2154 | " 1.0 \n",
2155 | " 0.0 \n",
2156 | " 0.0 \n",
2157 | " 1.07541 \n",
2158 | " \n",
2159 | " \n",
2160 | " 82401 \n",
2161 | " 153.0 \n",
2162 | " 158.0 \n",
2163 | " -8.0 \n",
2164 | " 5.0 \n",
2165 | " -161.0 \n",
2166 | " 166.0 \n",
2167 | " 1.0 \n",
2168 | " 0.0 \n",
2169 | " 0.0 \n",
2170 | " 1.07749 \n",
2171 | " \n",
2172 | " \n",
2173 | " 91157 \n",
2174 | " 216.0 \n",
2175 | " 237.0 \n",
2176 | " -4.0 \n",
2177 | " 21.0 \n",
2178 | " -220.0 \n",
2179 | " 241.0 \n",
2180 | " 1.0 \n",
2181 | " 0.0 \n",
2182 | " 0.0 \n",
2183 | " 1.09701 \n",
2184 | " \n",
2185 | " \n",
2186 | " 95328 \n",
2187 | " 165.0 \n",
2188 | " 181.0 \n",
2189 | " -2.0 \n",
2190 | " 16.0 \n",
2191 | " -167.0 \n",
2192 | " 183.0 \n",
2193 | " 0.0 \n",
2194 | " 1.0 \n",
2195 | " 0.0 \n",
2196 | " 1.07819 \n",
2197 | " \n",
2198 | " \n",
2199 | " 103810 \n",
2200 | " 190.0 \n",
2201 | " 201.0 \n",
2202 | " 0.0 \n",
2203 | " 11.0 \n",
2204 | " -190.0 \n",
2205 | " 201.0 \n",
2206 | " 1.0 \n",
2207 | " 0.0 \n",
2208 | " 0.0 \n",
2209 | " 1.09081 \n",
2210 | " \n",
2211 | " \n",
2212 | " 115743 \n",
2213 | " 169.0 \n",
2214 | " 194.0 \n",
2215 | " -7.0 \n",
2216 | " 25.0 \n",
2217 | " -176.0 \n",
2218 | " 201.0 \n",
2219 | " 1.0 \n",
2220 | " 0.0 \n",
2221 | " 0.0 \n",
2222 | " 1.07821 \n",
2223 | " \n",
2224 | " \n",
2225 | " 149953 \n",
2226 | " 281.0 \n",
2227 | " 293.0 \n",
2228 | " -2.0 \n",
2229 | " 12.0 \n",
2230 | " -283.0 \n",
2231 | " 295.0 \n",
2232 | " 1.0 \n",
2233 | " 0.0 \n",
2234 | " 0.0 \n",
2235 | " 1.09806 \n",
2236 | " \n",
2237 | " \n",
2238 | " 164692 \n",
2239 | " 194.0 \n",
2240 | " 250.0 \n",
2241 | " -139.0 \n",
2242 | " 56.0 \n",
2243 | " -333.0 \n",
2244 | " 389.0 \n",
2245 | " 0.0 \n",
2246 | " 1.0 \n",
2247 | " 0.0 \n",
2248 | " 1.13476 \n",
2249 | " \n",
2250 | " \n",
2251 | " 164755 \n",
2252 | " 161.0 \n",
2253 | " 163.0 \n",
2254 | " -13.0 \n",
2255 | " 2.0 \n",
2256 | " -174.0 \n",
2257 | " 176.0 \n",
2258 | " 1.0 \n",
2259 | " 0.0 \n",
2260 | " 0.0 \n",
2261 | " 1.13653 \n",
2262 | " \n",
2263 | " \n",
2264 | " 194261 \n",
2265 | " 167.0 \n",
2266 | " 168.0 \n",
2267 | " 0.0 \n",
2268 | " 1.0 \n",
2269 | " -167.0 \n",
2270 | " 168.0 \n",
2271 | " 1.0 \n",
2272 | " 0.0 \n",
2273 | " 0.0 \n",
2274 | " 1.13310 \n",
2275 | " \n",
2276 | " \n",
2277 | " 249299 \n",
2278 | " 192.0 \n",
2279 | " 247.0 \n",
2280 | " -5.0 \n",
2281 | " 55.0 \n",
2282 | " -197.0 \n",
2283 | " 252.0 \n",
2284 | " 1.0 \n",
2285 | " 0.0 \n",
2286 | " 0.0 \n",
2287 | " 1.19560 \n",
2288 | " \n",
2289 | " \n",
2290 | " 253537 \n",
2291 | " 165.0 \n",
2292 | " 187.0 \n",
2293 | " -4.0 \n",
2294 | " 22.0 \n",
2295 | " -169.0 \n",
2296 | " 191.0 \n",
2297 | " 0.0 \n",
2298 | " 1.0 \n",
2299 | " 0.0 \n",
2300 | " 1.18196 \n",
2301 | " \n",
2302 | " \n",
2303 | " 259284 \n",
2304 | " 206.0 \n",
2305 | " 221.0 \n",
2306 | " -58.0 \n",
2307 | " 15.0 \n",
2308 | " -264.0 \n",
2309 | " 279.0 \n",
2310 | " 1.0 \n",
2311 | " 0.0 \n",
2312 | " 0.0 \n",
2313 | " 1.18676 \n",
2314 | " \n",
2315 | " \n",
2316 | " 314562 \n",
2317 | " 159.0 \n",
2318 | " 159.0 \n",
2319 | " 0.0 \n",
2320 | " 0.0 \n",
2321 | " -159.0 \n",
2322 | " 159.0 \n",
2323 | " 1.0 \n",
2324 | " 0.0 \n",
2325 | " 0.0 \n",
2326 | " 1.16759 \n",
2327 | " \n",
2328 | " \n",
2329 | " 314619 \n",
2330 | " 274.0 \n",
2331 | " 278.0 \n",
2332 | " -2.0 \n",
2333 | " 4.0 \n",
2334 | " -276.0 \n",
2335 | " 280.0 \n",
2336 | " 1.0 \n",
2337 | " 0.0 \n",
2338 | " 0.0 \n",
2339 | " 1.16360 \n",
2340 | " \n",
2341 | " \n",
2342 | " 318216 \n",
2343 | " 156.0 \n",
2344 | " 156.0 \n",
2345 | " 0.0 \n",
2346 | " 0.0 \n",
2347 | " -156.0 \n",
2348 | " 156.0 \n",
2349 | " 1.0 \n",
2350 | " 0.0 \n",
2351 | " 0.0 \n",
2352 | " 1.18622 \n",
2353 | " \n",
2354 | " \n",
2355 | "
\n",
2356 | "
"
2357 | ],
2358 | "text/plain": [
2359 | " open_close open_low open_high close_low close_high high_low up* \\\n",
2360 | "62514 225.0 230.0 -6.0 5.0 -231.0 236.0 1.0 \n",
2361 | "63952 170.0 174.0 -46.0 4.0 -216.0 220.0 1.0 \n",
2362 | "66858 200.0 200.0 0.0 0.0 -200.0 200.0 1.0 \n",
2363 | "67476 229.0 265.0 -305.0 36.0 -534.0 570.0 0.0 \n",
2364 | "67480 200.0 221.0 -14.0 21.0 -214.0 235.0 1.0 \n",
2365 | "68188 159.0 160.0 -2.0 1.0 -161.0 162.0 0.0 \n",
2366 | "68192 349.0 455.0 -6.0 106.0 -355.0 461.0 1.0 \n",
2367 | "72453 374.0 382.0 0.0 8.0 -374.0 382.0 1.0 \n",
2368 | "72518 184.0 184.0 0.0 0.0 -184.0 184.0 1.0 \n",
2369 | "72522 170.0 176.0 0.0 6.0 -170.0 176.0 0.0 \n",
2370 | "72523 190.0 237.0 0.0 47.0 -190.0 237.0 0.0 \n",
2371 | "72528 325.0 325.0 -9.0 0.0 -334.0 334.0 1.0 \n",
2372 | "72618 153.0 163.0 -13.0 10.0 -166.0 176.0 1.0 \n",
2373 | "72641 190.0 214.0 -20.0 24.0 -210.0 234.0 1.0 \n",
2374 | "72650 152.0 212.0 0.0 60.0 -152.0 212.0 1.0 \n",
2375 | "72782 151.0 151.0 0.0 0.0 -151.0 151.0 0.0 \n",
2376 | "74073 156.0 159.0 0.0 3.0 -156.0 159.0 0.0 \n",
2377 | "75769 153.0 196.0 -4.0 43.0 -157.0 200.0 1.0 \n",
2378 | "78759 153.0 153.0 -1.0 0.0 -154.0 154.0 0.0 \n",
2379 | "79263 161.0 175.0 -10.0 14.0 -171.0 185.0 1.0 \n",
2380 | "79271 154.0 171.0 -12.0 17.0 -166.0 183.0 0.0 \n",
2381 | "79301 193.0 197.0 -9.0 4.0 -202.0 206.0 0.0 \n",
2382 | "79505 175.0 177.0 0.0 2.0 -175.0 177.0 0.0 \n",
2383 | "79522 176.0 176.0 -5.0 0.0 -181.0 181.0 1.0 \n",
2384 | "79590 162.0 195.0 -3.0 33.0 -165.0 198.0 0.0 \n",
2385 | "79771 173.0 181.0 0.0 8.0 -173.0 181.0 0.0 \n",
2386 | "79800 207.0 210.0 -11.0 3.0 -218.0 221.0 1.0 \n",
2387 | "82380 195.0 203.0 -6.0 8.0 -201.0 209.0 1.0 \n",
2388 | "82401 153.0 158.0 -8.0 5.0 -161.0 166.0 1.0 \n",
2389 | "91157 216.0 237.0 -4.0 21.0 -220.0 241.0 1.0 \n",
2390 | "95328 165.0 181.0 -2.0 16.0 -167.0 183.0 0.0 \n",
2391 | "103810 190.0 201.0 0.0 11.0 -190.0 201.0 1.0 \n",
2392 | "115743 169.0 194.0 -7.0 25.0 -176.0 201.0 1.0 \n",
2393 | "149953 281.0 293.0 -2.0 12.0 -283.0 295.0 1.0 \n",
2394 | "164692 194.0 250.0 -139.0 56.0 -333.0 389.0 0.0 \n",
2395 | "164755 161.0 163.0 -13.0 2.0 -174.0 176.0 1.0 \n",
2396 | "194261 167.0 168.0 0.0 1.0 -167.0 168.0 1.0 \n",
2397 | "249299 192.0 247.0 -5.0 55.0 -197.0 252.0 1.0 \n",
2398 | "253537 165.0 187.0 -4.0 22.0 -169.0 191.0 0.0 \n",
2399 | "259284 206.0 221.0 -58.0 15.0 -264.0 279.0 1.0 \n",
2400 | "314562 159.0 159.0 0.0 0.0 -159.0 159.0 1.0 \n",
2401 | "314619 274.0 278.0 -2.0 4.0 -276.0 280.0 1.0 \n",
2402 | "318216 156.0 156.0 0.0 0.0 -156.0 156.0 1.0 \n",
2403 | "\n",
2404 | " down* no_move* open* \n",
2405 | "62514 0.0 0.0 1.11575 \n",
2406 | "63952 0.0 0.0 1.11011 \n",
2407 | "66858 0.0 0.0 1.12902 \n",
2408 | "67476 1.0 0.0 1.14440 \n",
2409 | "67480 0.0 0.0 1.14345 \n",
2410 | "68188 1.0 0.0 1.14619 \n",
2411 | "68192 0.0 0.0 1.14162 \n",
2412 | "72453 0.0 0.0 1.12120 \n",
2413 | "72518 0.0 0.0 1.12207 \n",
2414 | "72522 1.0 0.0 1.11985 \n",
2415 | "72523 1.0 0.0 1.11795 \n",
2416 | "72528 0.0 0.0 1.11344 \n",
2417 | "72618 0.0 0.0 1.10934 \n",
2418 | "72641 0.0 0.0 1.10605 \n",
2419 | "72650 0.0 0.0 1.10614 \n",
2420 | "72782 1.0 0.0 1.11966 \n",
2421 | "74073 1.0 0.0 1.10942 \n",
2422 | "75769 0.0 0.0 1.11639 \n",
2423 | "78759 1.0 0.0 1.09474 \n",
2424 | "79263 0.0 0.0 1.08754 \n",
2425 | "79271 1.0 0.0 1.08629 \n",
2426 | "79301 1.0 0.0 1.08295 \n",
2427 | "79505 1.0 0.0 1.07425 \n",
2428 | "79522 0.0 0.0 1.07424 \n",
2429 | "79590 1.0 0.0 1.07986 \n",
2430 | "79771 1.0 0.0 1.07055 \n",
2431 | "79800 0.0 0.0 1.06826 \n",
2432 | "82380 0.0 0.0 1.07541 \n",
2433 | "82401 0.0 0.0 1.07749 \n",
2434 | "91157 0.0 0.0 1.09701 \n",
2435 | "95328 1.0 0.0 1.07819 \n",
2436 | "103810 0.0 0.0 1.09081 \n",
2437 | "115743 0.0 0.0 1.07821 \n",
2438 | "149953 0.0 0.0 1.09806 \n",
2439 | "164692 1.0 0.0 1.13476 \n",
2440 | "164755 0.0 0.0 1.13653 \n",
2441 | "194261 0.0 0.0 1.13310 \n",
2442 | "249299 0.0 0.0 1.19560 \n",
2443 | "253537 1.0 0.0 1.18196 \n",
2444 | "259284 0.0 0.0 1.18676 \n",
2445 | "314562 0.0 0.0 1.16759 \n",
2446 | "314619 0.0 0.0 1.16360 \n",
2447 | "318216 0.0 0.0 1.18622 "
2448 | ]
2449 | },
2450 | "execution_count": 21,
2451 | "metadata": {},
2452 | "output_type": "execute_result"
2453 | }
2454 | ],
2455 | "source": [
2456 | "big_drop = dataset[dataset[\"open_close\"] > 150]\n",
2457 | "big_drop"
2458 | ]
2459 | },
2460 | {
2461 | "cell_type": "code",
2462 | "execution_count": 22,
2463 | "metadata": {},
2464 | "outputs": [
2465 | {
2466 | "data": {
2467 | "text/plain": [
2468 | "up* down* no_move*\n",
2469 | "1.0 0.0 0.0 28\n",
2470 | "0.0 1.0 0.0 15\n",
2471 | "dtype: int64"
2472 | ]
2473 | },
2474 | "execution_count": 22,
2475 | "metadata": {},
2476 | "output_type": "execute_result"
2477 | }
2478 | ],
2479 | "source": [
2480 | "#get the number of up and down movement in price after a big downward movement in price\n",
2481 | "big_drop[[\"up*\", \"down*\", \"no_move*\"]].value_counts()"
2482 | ]
2483 | },
2484 | {
2485 | "cell_type": "markdown",
2486 | "metadata": {},
2487 | "source": [
2488 | "### Result"
2489 | ]
2490 | },
2491 | {
2492 | "cell_type": "markdown",
2493 | "metadata": {},
2494 | "source": [
2495 | "From the information above we had 28 upward movement in price and 15 downward movement in price total \n",
2496 | "candles = 28 + 15 = 43 \n",
2497 | "percentage up = (100 * 28)/(63) = 65.11% \n",
2498 | "percentage down = (100 *24)/(63) = 34.89% \n",
2499 | "so after a 15 pips rise in price for a one minute time frame of EURUSD there was a 65.11% chance that the next candle would be bullish and 34.89% chance of a bearish candle "
2500 | ]
2501 | },
2502 | {
2503 | "cell_type": "code",
2504 | "execution_count": 32,
2505 | "metadata": {},
2506 | "outputs": [
2507 | {
2508 | "data": {
2509 | "text/plain": [
2510 | ""
2511 | ]
2512 | },
2513 | "execution_count": 32,
2514 | "metadata": {},
2515 | "output_type": "execute_result"
2516 | },
2517 | {
2518 | "data": {
2519 | "image/png": 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\n",
2520 | "text/plain": [
2521 | ""
2522 | ]
2523 | },
2524 | "metadata": {},
2525 | "output_type": "display_data"
2526 | }
2527 | ],
2528 | "source": [
2529 | "sns.swarmplot(x=big_drop['up*'], y=big_drop['open_close'])"
2530 | ]
2531 | },
2532 | {
2533 | "cell_type": "code",
2534 | "execution_count": 33,
2535 | "metadata": {},
2536 | "outputs": [
2537 | {
2538 | "data": {
2539 | "text/plain": [
2540 | ""
2541 | ]
2542 | },
2543 | "execution_count": 33,
2544 | "metadata": {},
2545 | "output_type": "execute_result"
2546 | },
2547 | {
2548 | "data": {
2549 | "image/png": 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\n",
2550 | "text/plain": [
2551 | ""
2552 | ]
2553 | },
2554 | "metadata": {},
2555 | "output_type": "display_data"
2556 | }
2557 | ],
2558 | "source": [
2559 | "sns.swarmplot(x=big_drop['down*'], y=big_drop['open_close'])"
2560 | ]
2561 | },
2562 | {
2563 | "cell_type": "markdown",
2564 | "metadata": {},
2565 | "source": [
2566 | "### Anaylyse candles after a prior large rise in prices up to 15 pips in a minute"
2567 | ]
2568 | },
2569 | {
2570 | "cell_type": "code",
2571 | "execution_count": 24,
2572 | "metadata": {},
2573 | "outputs": [
2574 | {
2575 | "data": {
2576 | "text/html": [
2577 | "\n",
2578 | "\n",
2591 | "
\n",
2592 | " \n",
2593 | " \n",
2594 | " \n",
2595 | " open_close \n",
2596 | " open_low \n",
2597 | " open_high \n",
2598 | " close_low \n",
2599 | " close_high \n",
2600 | " high_low \n",
2601 | " up* \n",
2602 | " down* \n",
2603 | " no_move* \n",
2604 | " open* \n",
2605 | " \n",
2606 | " \n",
2607 | " \n",
2608 | " \n",
2609 | " 9203 \n",
2610 | " -170.0 \n",
2611 | " 0.0 \n",
2612 | " -173.0 \n",
2613 | " 170.0 \n",
2614 | " -3.0 \n",
2615 | " 173.0 \n",
2616 | " 0.0 \n",
2617 | " 1.0 \n",
2618 | " 0.0 \n",
2619 | " 1.11123 \n",
2620 | " \n",
2621 | " \n",
2622 | " 22149 \n",
2623 | " -165.0 \n",
2624 | " 3.0 \n",
2625 | " -168.0 \n",
2626 | " 168.0 \n",
2627 | " -3.0 \n",
2628 | " 171.0 \n",
2629 | " 0.0 \n",
2630 | " 1.0 \n",
2631 | " 0.0 \n",
2632 | " 1.11084 \n",
2633 | " \n",
2634 | " \n",
2635 | " 62512 \n",
2636 | " -562.0 \n",
2637 | " 0.0 \n",
2638 | " -590.0 \n",
2639 | " 562.0 \n",
2640 | " -28.0 \n",
2641 | " 590.0 \n",
2642 | " 0.0 \n",
2643 | " 1.0 \n",
2644 | " 0.0 \n",
2645 | " 1.11802 \n",
2646 | " \n",
2647 | " \n",
2648 | " 67475 \n",
2649 | " -665.0 \n",
2650 | " 2.0 \n",
2651 | " -665.0 \n",
2652 | " 667.0 \n",
2653 | " 0.0 \n",
2654 | " 667.0 \n",
2655 | " 0.0 \n",
2656 | " 1.0 \n",
2657 | " 0.0 \n",
2658 | " 1.14659 \n",
2659 | " \n",
2660 | " \n",
2661 | " 67479 \n",
2662 | " -211.0 \n",
2663 | " 90.0 \n",
2664 | " -251.0 \n",
2665 | " 301.0 \n",
2666 | " -40.0 \n",
2667 | " 341.0 \n",
2668 | " 0.0 \n",
2669 | " 1.0 \n",
2670 | " 0.0 \n",
2671 | " 1.14546 \n",
2672 | " \n",
2673 | " \n",
2674 | " ... \n",
2675 | " ... \n",
2676 | " ... \n",
2677 | " ... \n",
2678 | " ... \n",
2679 | " ... \n",
2680 | " ... \n",
2681 | " ... \n",
2682 | " ... \n",
2683 | " ... \n",
2684 | " ... \n",
2685 | " \n",
2686 | " \n",
2687 | " 314747 \n",
2688 | " -198.0 \n",
2689 | " 0.0 \n",
2690 | " -200.0 \n",
2691 | " 198.0 \n",
2692 | " -2.0 \n",
2693 | " 200.0 \n",
2694 | " 0.0 \n",
2695 | " 1.0 \n",
2696 | " 0.0 \n",
2697 | " 1.16757 \n",
2698 | " \n",
2699 | " \n",
2700 | " 317344 \n",
2701 | " -219.0 \n",
2702 | " 3.0 \n",
2703 | " -274.0 \n",
2704 | " 222.0 \n",
2705 | " -55.0 \n",
2706 | " 277.0 \n",
2707 | " 0.0 \n",
2708 | " 1.0 \n",
2709 | " 0.0 \n",
2710 | " 1.18409 \n",
2711 | " \n",
2712 | " \n",
2713 | " 321098 \n",
2714 | " -168.0 \n",
2715 | " 10.0 \n",
2716 | " -194.0 \n",
2717 | " 178.0 \n",
2718 | " -26.0 \n",
2719 | " 204.0 \n",
2720 | " 0.0 \n",
2721 | " 1.0 \n",
2722 | " 0.0 \n",
2723 | " 1.18248 \n",
2724 | " \n",
2725 | " \n",
2726 | " 352640 \n",
2727 | " -188.0 \n",
2728 | " 19.0 \n",
2729 | " -260.0 \n",
2730 | " 207.0 \n",
2731 | " -72.0 \n",
2732 | " 279.0 \n",
2733 | " 1.0 \n",
2734 | " 0.0 \n",
2735 | " 0.0 \n",
2736 | " 1.21072 \n",
2737 | " \n",
2738 | " \n",
2739 | " 358138 \n",
2740 | " -161.0 \n",
2741 | " 0.0 \n",
2742 | " -190.0 \n",
2743 | " 161.0 \n",
2744 | " -29.0 \n",
2745 | " 190.0 \n",
2746 | " 1.0 \n",
2747 | " 0.0 \n",
2748 | " 0.0 \n",
2749 | " 1.21844 \n",
2750 | " \n",
2751 | " \n",
2752 | "
\n",
2753 | "
63 rows × 10 columns
\n",
2754 | "
"
2755 | ],
2756 | "text/plain": [
2757 | " open_close open_low open_high close_low close_high high_low up* \\\n",
2758 | "9203 -170.0 0.0 -173.0 170.0 -3.0 173.0 0.0 \n",
2759 | "22149 -165.0 3.0 -168.0 168.0 -3.0 171.0 0.0 \n",
2760 | "62512 -562.0 0.0 -590.0 562.0 -28.0 590.0 0.0 \n",
2761 | "67475 -665.0 2.0 -665.0 667.0 0.0 667.0 0.0 \n",
2762 | "67479 -211.0 90.0 -251.0 301.0 -40.0 341.0 0.0 \n",
2763 | "... ... ... ... ... ... ... ... \n",
2764 | "314747 -198.0 0.0 -200.0 198.0 -2.0 200.0 0.0 \n",
2765 | "317344 -219.0 3.0 -274.0 222.0 -55.0 277.0 0.0 \n",
2766 | "321098 -168.0 10.0 -194.0 178.0 -26.0 204.0 0.0 \n",
2767 | "352640 -188.0 19.0 -260.0 207.0 -72.0 279.0 1.0 \n",
2768 | "358138 -161.0 0.0 -190.0 161.0 -29.0 190.0 1.0 \n",
2769 | "\n",
2770 | " down* no_move* open* \n",
2771 | "9203 1.0 0.0 1.11123 \n",
2772 | "22149 1.0 0.0 1.11084 \n",
2773 | "62512 1.0 0.0 1.11802 \n",
2774 | "67475 1.0 0.0 1.14659 \n",
2775 | "67479 1.0 0.0 1.14546 \n",
2776 | "... ... ... ... \n",
2777 | "314747 1.0 0.0 1.16757 \n",
2778 | "317344 1.0 0.0 1.18409 \n",
2779 | "321098 1.0 0.0 1.18248 \n",
2780 | "352640 0.0 0.0 1.21072 \n",
2781 | "358138 0.0 0.0 1.21844 \n",
2782 | "\n",
2783 | "[63 rows x 10 columns]"
2784 | ]
2785 | },
2786 | "execution_count": 24,
2787 | "metadata": {},
2788 | "output_type": "execute_result"
2789 | }
2790 | ],
2791 | "source": [
2792 | "big_rise = dataset[dataset[\"open_close\"] < -150]\n",
2793 | "big_rise"
2794 | ]
2795 | },
2796 | {
2797 | "cell_type": "code",
2798 | "execution_count": 25,
2799 | "metadata": {},
2800 | "outputs": [
2801 | {
2802 | "data": {
2803 | "text/plain": [
2804 | "up* down* no_move*\n",
2805 | "0.0 1.0 0.0 38\n",
2806 | "1.0 0.0 0.0 24\n",
2807 | "0.0 0.0 1.0 1\n",
2808 | "dtype: int64"
2809 | ]
2810 | },
2811 | "execution_count": 25,
2812 | "metadata": {},
2813 | "output_type": "execute_result"
2814 | }
2815 | ],
2816 | "source": [
2817 | "big_rise[[\"up*\", \"down*\", \"no_move*\"]].value_counts()"
2818 | ]
2819 | },
2820 | {
2821 | "cell_type": "markdown",
2822 | "metadata": {},
2823 | "source": [
2824 | "From the information above we had 38 upward movement in price and 24 downward movement in price total \n",
2825 | "and 1 no move that is open and close are equal\n",
2826 | "candles = 38 + 24 + 1 = 63 \n",
2827 | "percentage up = (100 * 38)/(63) = 60.32% \n",
2828 | "percentage down = (100 *24)/(63) = 38.10% \n",
2829 | "percentage no move = (100 *1)/(63) = 1.58% \n",
2830 | "so after a 16 pips rise in price for a one minute time frame of EURUSD there was a 60.32% chance that the next candle would be bullish and 38.19% chance of a bearish candle and 1.58% chance open and close are equal "
2831 | ]
2832 | },
2833 | {
2834 | "cell_type": "code",
2835 | "execution_count": 34,
2836 | "metadata": {},
2837 | "outputs": [
2838 | {
2839 | "data": {
2840 | "text/plain": [
2841 | ""
2842 | ]
2843 | },
2844 | "execution_count": 34,
2845 | "metadata": {},
2846 | "output_type": "execute_result"
2847 | },
2848 | {
2849 | "data": {
2850 | "image/png": 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\n",
2851 | "text/plain": [
2852 | ""
2853 | ]
2854 | },
2855 | "metadata": {},
2856 | "output_type": "display_data"
2857 | }
2858 | ],
2859 | "source": [
2860 | "sns.swarmplot(x=big_rise['up*'], y=big_rise['open_close'])"
2861 | ]
2862 | },
2863 | {
2864 | "cell_type": "code",
2865 | "execution_count": 35,
2866 | "metadata": {},
2867 | "outputs": [
2868 | {
2869 | "data": {
2870 | "text/plain": [
2871 | ""
2872 | ]
2873 | },
2874 | "execution_count": 35,
2875 | "metadata": {},
2876 | "output_type": "execute_result"
2877 | },
2878 | {
2879 | "data": {
2880 | "image/png": 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\n",
2881 | "text/plain": [
2882 | ""
2883 | ]
2884 | },
2885 | "metadata": {},
2886 | "output_type": "display_data"
2887 | }
2888 | ],
2889 | "source": [
2890 | "sns.swarmplot(x=big_rise['down*'], y=big_rise['open_close'])"
2891 | ]
2892 | },
2893 | {
2894 | "cell_type": "code",
2895 | "execution_count": 36,
2896 | "metadata": {},
2897 | "outputs": [
2898 | {
2899 | "data": {
2900 | "text/plain": [
2901 | ""
2902 | ]
2903 | },
2904 | "execution_count": 36,
2905 | "metadata": {},
2906 | "output_type": "execute_result"
2907 | },
2908 | {
2909 | "data": {
2910 | "image/png": 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\n",
2911 | "text/plain": [
2912 | ""
2913 | ]
2914 | },
2915 | "metadata": {},
2916 | "output_type": "display_data"
2917 | }
2918 | ],
2919 | "source": [
2920 | "sns.swarmplot(x=big_rise['no_move*'], y=big_rise['open_close'])"
2921 | ]
2922 | },
2923 | {
2924 | "cell_type": "markdown",
2925 | "metadata": {},
2926 | "source": [
2927 | "# Bullish prior Patterns Analysis"
2928 | ]
2929 | },
2930 | {
2931 | "cell_type": "code",
2932 | "execution_count": null,
2933 | "metadata": {},
2934 | "outputs": [],
2935 | "source": [
2936 | "#concat move_df and future to create a new dataframe\n",
2937 | "full_data = pd.concat([data, move_df, future], axis = 1)\n",
2938 | "full_data.dropna(inplace=True)\n",
2939 | "full_data"
2940 | ]
2941 | },
2942 | {
2943 | "cell_type": "code",
2944 | "execution_count": null,
2945 | "metadata": {},
2946 | "outputs": [],
2947 | "source": [
2948 | "bullish = full_data[full_data[\"\"] == 1]\n",
2949 | "bullish"
2950 | ]
2951 | },
2952 | {
2953 | "cell_type": "code",
2954 | "execution_count": null,
2955 | "metadata": {},
2956 | "outputs": [],
2957 | "source": [
2958 | "high_wink = bullish[bullish[\"close_high\"] < -100]\n",
2959 | "high_wink"
2960 | ]
2961 | },
2962 | {
2963 | "cell_type": "code",
2964 | "execution_count": null,
2965 | "metadata": {},
2966 | "outputs": [],
2967 | "source": [
2968 | "sns.swarmplot(high_wink['down*'], high_wink['close_high'])"
2969 | ]
2970 | },
2971 | {
2972 | "cell_type": "code",
2973 | "execution_count": null,
2974 | "metadata": {},
2975 | "outputs": [],
2976 | "source": [
2977 | "high_wink[[\"up*\", \"down*\", \"no_move*\"]].value_counts()"
2978 | ]
2979 | },
2980 | {
2981 | "cell_type": "code",
2982 | "execution_count": null,
2983 | "metadata": {},
2984 | "outputs": [],
2985 | "source": [
2986 | "low_wink = bullish[bullish[\"open_low\"] > 100]\n",
2987 | "low_wink"
2988 | ]
2989 | },
2990 | {
2991 | "cell_type": "code",
2992 | "execution_count": null,
2993 | "metadata": {},
2994 | "outputs": [],
2995 | "source": [
2996 | "sns.swarmplot(low_wink['down*'], low_wink['open_low'])"
2997 | ]
2998 | },
2999 | {
3000 | "cell_type": "code",
3001 | "execution_count": null,
3002 | "metadata": {},
3003 | "outputs": [],
3004 | "source": [
3005 | "low_wink[[\"up*\", \"down*\", \"no_move*\"]].value_counts()"
3006 | ]
3007 | },
3008 | {
3009 | "cell_type": "code",
3010 | "execution_count": null,
3011 | "metadata": {},
3012 | "outputs": [],
3013 | "source": [
3014 | "full_data"
3015 | ]
3016 | },
3017 | {
3018 | "cell_type": "code",
3019 | "execution_count": null,
3020 | "metadata": {},
3021 | "outputs": [],
3022 | "source": [
3023 | "#Y = full_data[[\"up*\", \"down*\", \"no_move*\"]]\n",
3024 | "#Y.to_csv(\"Y.csv\", index=False)"
3025 | ]
3026 | },
3027 | {
3028 | "cell_type": "code",
3029 | "execution_count": null,
3030 | "metadata": {},
3031 | "outputs": [],
3032 | "source": [
3033 | "#X = move_df\n",
3034 | "#X.to_csv(\"X.csv\", index=False)"
3035 | ]
3036 | },
3037 | {
3038 | "cell_type": "code",
3039 | "execution_count": null,
3040 | "metadata": {},
3041 | "outputs": [],
3042 | "source": [
3043 | "bullish2 = full_data[full_data[\"\"] == 1]\n",
3044 | "bullish"
3045 | ]
3046 | },
3047 | {
3048 | "cell_type": "code",
3049 | "execution_count": null,
3050 | "metadata": {},
3051 | "outputs": [],
3052 | "source": []
3053 | }
3054 | ],
3055 | "metadata": {
3056 | "kernelspec": {
3057 | "display_name": "Python 3",
3058 | "language": "python",
3059 | "name": "python3"
3060 | },
3061 | "language_info": {
3062 | "codemirror_mode": {
3063 | "name": "ipython",
3064 | "version": 3
3065 | },
3066 | "file_extension": ".py",
3067 | "mimetype": "text/x-python",
3068 | "name": "python",
3069 | "nbconvert_exporter": "python",
3070 | "pygments_lexer": "ipython3",
3071 | "version": "3.8.5"
3072 | }
3073 | },
3074 | "nbformat": 4,
3075 | "nbformat_minor": 4
3076 | }
3077 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2021 Joshua Evuetapha
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # binary-options-analysis
2 | This research analyze the binary options market on EURUSD M1 in jupyter notebook
3 |
4 | ## Dependencies
5 |
6 | ```python
7 |
8 | pip install plotly
9 | pip install pandas
10 | pip install numpy
11 | pip install seaborn
12 | '''
13 |
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