├── LocalLog.txt
├── .gitignore
├── README.md
├── __pycache__
    ├── Error.cpython-37.pyc
    ├── Swings.cpython-37.pyc
    └── ElliotAnalyzer.cpython-37.pyc
├── Error.py
├── PairSelector.py
├── summary_analysis.txt
├── Handler_Config.conf
├── Pair_Analysis.txt
├── AnalyzerConfig.conf
├── Handler.py
├── Swings.py
├── ElliotAnalyzer.py
└── LICENSE


/LocalLog.txt:
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1 | 


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/.gitignore:
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1 | 
2 | *.pyc
3 | __pycache__/*
4 | 


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/README.md:
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1 | # ElliotWaveAnalysis
2 | Elliot Wave Analyzer for OHLC data
3 | 


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/__pycache__/Error.cpython-37.pyc:
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https://raw.githubusercontent.com/Wkemery/ElliotWaveAnalysis/HEAD/__pycache__/Error.cpython-37.pyc


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/__pycache__/Swings.cpython-37.pyc:
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https://raw.githubusercontent.com/Wkemery/ElliotWaveAnalysis/HEAD/__pycache__/Swings.cpython-37.pyc


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/__pycache__/ElliotAnalyzer.cpython-37.pyc:
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https://raw.githubusercontent.com/Wkemery/ElliotWaveAnalysis/HEAD/__pycache__/ElliotAnalyzer.cpython-37.pyc


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/Error.py:
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1 | from __future__ import print_function
2 | import sys
3 | 
4 | 
5 | def eprint(*args, **kwargs):
6 |     print('\t*Error Occured*',file=sys.stderr)
7 |     print(*args, file=sys.stderr, **kwargs)
8 | 


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/PairSelector.py:
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 1 | import os
 2 | 
 3 | FOREX_DATA_PATH = "C:\\Users\\wyatt\\Documents\\ForexData"
 4 | 
 5 | outfile = open('Pair_Analysis.txt', 'w')
 6 | FX_pairs = os.listdir(FOREX_DATA_PATH)
 7 | 
 8 | for pair in FX_pairs:
 9 |     outfile.write(pair + "_H1" + "\n")
10 |     outfile.write(pair + "_H4" + "\n")
11 |     outfile.write(pair + "_D" + "\n")
12 | 


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/summary_analysis.txt:
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 1 | AUDCAD_D	Wave5	Minimum	IT
 2 | CADJPY_H1	Wave4	Minimum	ST
 3 | EURCHF_H1	Wave4	Minimum	ST
 4 | EURJPY_H1	Wave4	Minimum	IT
 5 | EURJPY_H1	Wave4	Minimum	LT
 6 | EURJPY_H4	Wave4	Minimum	ST
 7 | EURUSD_H1	Wave4	Minimum	LT
 8 | EURUSD_H4	Wave4	Typical	ST
 9 | USDJPY_H1	Wave4	Minimum	IT
10 | USDJPY_H1	Wave4	Minimum	LT
11 | USDJPY_H4	Wave4	Minimum	ST
12 | 


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/Handler_Config.conf:
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 1 | [Short_Term]
 2 | ATR_Period: 5
 3 | Time_Factor: 3
 4 | Price_Factor: 1.5
 5 | Reference_Price: HighLow
 6 | 
 7 | [Intermediate_Term]
 8 | ATR_Period: 10
 9 | Time_Factor: 5
10 | Price_Factor: 2.5
11 | Reference_Price: HighLow
12 | 
13 | [Long_Term]
14 | ATR_Period: 10
15 | Time_Factor: 7
16 | Price_Factor: 3.5
17 | Reference_Price: HighLow
18 | 
19 | [Major_Term]
20 | ATR_Period: 15
21 | Time_Factor: 10
22 | Price_Factor: 5
23 | Reference_Price: HighLow
24 | 
25 | [Other]
26 | Typical: 0
27 | 


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/Pair_Analysis.txt:
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 1 | AUDCAD_H1
 2 | AUDCAD_H4
 3 | AUDCAD_D
 4 | AUDCHF_H1
 5 | AUDCHF_H4
 6 | AUDCHF_D
 7 | AUDJPY_H1
 8 | AUDJPY_H4
 9 | AUDJPY_D
10 | AUDNZD_H1
11 | AUDNZD_H4
12 | AUDNZD_D
13 | AUDUSD_H1
14 | AUDUSD_H4
15 | AUDUSD_D
16 | CADCHF_H1
17 | CADCHF_H4
18 | CADCHF_D
19 | CADJPY_H1
20 | CADJPY_H4
21 | CADJPY_D
22 | CHFJPY_H1
23 | CHFJPY_H4
24 | CHFJPY_D
25 | EURAUD_H1
26 | EURAUD_H4
27 | EURAUD_D
28 | EURCAD_H1
29 | EURCAD_H4
30 | EURCAD_D
31 | EURCHF_H1
32 | EURCHF_H4
33 | EURCHF_D
34 | EURGBP_H1
35 | EURGBP_H4
36 | EURGBP_D
37 | EURJPY_H1
38 | EURJPY_H4
39 | EURJPY_D
40 | EURNZD_H1
41 | EURNZD_H4
42 | EURNZD_D
43 | EURUSD_H1
44 | EURUSD_H4
45 | EURUSD_D
46 | GBPAUD_H1
47 | GBPAUD_H4
48 | GBPAUD_D
49 | GBPCAD_H1
50 | GBPCAD_H4
51 | GBPCAD_D
52 | GBPCHF_H1
53 | GBPCHF_H4
54 | GBPCHF_D
55 | GBPJPY_H1
56 | GBPJPY_H4
57 | GBPJPY_D
58 | GBPNZD_H1
59 | GBPNZD_H4
60 | GBPNZD_D
61 | GBPUSD_H1
62 | GBPUSD_H4
63 | GBPUSD_D
64 | NZDCAD_H1
65 | NZDCAD_H4
66 | NZDCAD_D
67 | NZDCHF_H1
68 | NZDCHF_H4
69 | NZDCHF_D
70 | NZDJPY_H1
71 | NZDJPY_H4
72 | NZDJPY_D
73 | NZDUSD_H1
74 | NZDUSD_H4
75 | NZDUSD_D
76 | USDCAD_H1
77 | USDCAD_H4
78 | USDCAD_D
79 | USDCHF_H1
80 | USDCHF_H4
81 | USDCHF_D
82 | USDJPY_H1
83 | USDJPY_H4
84 | USDJPY_D
85 | 


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/AnalyzerConfig.conf:
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 1 | [Wave2]
 2 | inret_wave1_min: .382
 3 | inret_wave1_typical_min: .50
 4 | inret_wave1_typical_max: .618
 5 | inret_wave1_max: .786
 6 | 
 7 | [Wave3]
 8 | app_wave1_min: 1.000
 9 | app_wave1_typical: 1.618
10 | app_wave1_max: 2.618
11 | exret_wave2_min: 1.618
12 | exret_wave2_typical: 2.618
13 | exret_wave2_max: 4.236
14 | 
15 | [Wave4]
16 | ret_wave3_min: .236
17 | ret_wave3_typical: .382
18 | ret_wave3_max: .50
19 | ret_wave1_3_min: .236
20 | ret_wave1_3_typical: .382
21 | ret_wave1_3_max: .50
22 | 
23 | [Wave5]
24 | app_wave1_3_min: .382
25 | app_wave1_3_typical: .618
26 | app_wave1_3_max: 1.00
27 | app_wave_1: 1.00
28 | exret_wave4_min: 1.27
29 | exret_wave4_typical: 1.62
30 | exret_wave4_max: 2.62
31 | 
32 | [WaveC]
33 | inret_prior_trend_min: .382
34 | inret_prior_trend_typical_min: .50
35 | inret_prior_trend_typical_max: .618
36 | inret_prior_trend_max: .786
37 | app_wavea_min: .618
38 | app_wavea_typical: 1.00
39 | app_wavea_max: 1.62
40 | exret_waveb_min: 1.27
41 | exret_waveb_typical: 1.62
42 | exret_waveb_max: 2.62
43 | proper_trend: 1
44 | mini_trend: 0
45 | little_trend: 1
46 | 
47 | [Analysis_Type]
48 | Wave2: 0
49 | Wave3: 0
50 | Wave4: 1
51 | Wave5: 1
52 | WaveC: 1
53 | Gartley: 0
54 | Butterfly: 0
55 | 


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/Handler.py:
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 1 | import pandas as pd
 2 | from Swings import *
 3 | from ElliotAnalyzer import *
 4 | import os
 5 | import time
 6 | import configparser
 7 | import shutil
 8 | 
 9 | FOREX_DATA_PATH = "C:\\Users\\wyatt\\Documents\\ForexData"
10 | GRAPHS_PATH = "C:\\Users\\wyatt\\Documents\\ForexGraphs"
11 | ANALYSIS_SUMMARY_FILE = "summary_analysis.txt"
12 | CONFIG_FILE= "Handler_Config.conf"
13 | PAIRS_FILE = "Pair_Analysis.txt"
14 | TYPICAL = True
15 | 
16 | #TODO: add support for other config options to do big multiconfig, multitimeframe analysis at once
17 | ########################################################################################################################
18 | 
19 | def config_section_map(config, section):
20 |     dict1 = {}
21 |     options = config.options(section)
22 |     for option in options:
23 |         try:
24 |             dict1[option] = config.get(section, option)
25 |             if dict1[option] == -1:
26 |                 print("skip: %s" % option)
27 |         except:
28 |             print("exception on %s!" % option)
29 |             dict1[option] = None
30 |     return dict1
31 | 
32 | #Read in config
33 | config = configparser.ConfigParser()
34 | config.read(CONFIG_FILE)
35 | ST_config = config_section_map(config, "Short_Term")
36 | IT_config = config_section_map(config, "Intermediate_Term")
37 | LT_config = config_section_map(config, "Long_Term")
38 | MT_config = config_section_map(config, "Major_Term")
39 | config_list = [(ST_config, "ST"), (IT_config, "IT"), (LT_config, "LT"), (MT_config, "MT")] #TODO: determine which configs to use this time
40 | 
41 | #Set up analysis parameters
42 | typical = config_section_map(config, "Other")["typical"]
43 | 
44 | ########################################################################################################################
45 | 
46 | #Get the pairs to analyze
47 | with open(PAIRS_FILE, 'r') as infile:
48 |     pairs_to_analyze = infile.read().splitlines()
49 | 
50 | outfile = open(ANALYSIS_SUMMARY_FILE, 'w')
51 | 
52 | #Clear old graphs
53 | for file in os.listdir(GRAPHS_PATH):
54 |     os.remove(GRAPHS_PATH + "\\" + file)
55 | 
56 | ########################################################################################################################
57 | #Begin analyzing Pairs
58 | for pair in pairs_to_analyze:
59 |     pairname, pairtime = pair.split("_")
60 |     forex_name_template = FOREX_DATA_PATH + "\\" + pairname + "\\"
61 |     forex_data_file = forex_name_template + pair + ".csv"
62 | 
63 |     for current_config, config_name in config_list:
64 |         forex_swing_file =  forex_name_template + pair + "_swings_" + current_config["atr_period"] + "_" + current_config["time_factor"] + "_" + current_config["price_factor"] + ".csv"
65 | 
66 |         sg = Swing_Generator(forex_data_file,forex_swing_file, current_config)
67 |         if(os.path.isfile(forex_swing_file)):
68 |             sg.update_swings()
69 |         else:
70 |             sg.generate_swings()
71 | 
72 |         ea = Elliot_Analyzer(pair, forex_swing_file, forex_data_file)
73 |         analysis_summary = ea.analyze()
74 |         for result in analysis_summary:
75 |             if typical == "1":
76 |                 if ea.wave_data[result][1] != "Minimum":
77 |                     ea.export_graphs(GRAPHS_PATH + "\\" + pair + "_" + config_name)
78 |                     outfile.write(pair + "\t" + result + "\t" + ea.wave_data[result][1] + "\t" + config_name + "\n")
79 |             else:
80 |                 ea.export_graphs(GRAPHS_PATH + "\\" + pair + "_" + config_name)
81 |                 outfile.write(pair + "\t" + result + "\t" + ea.wave_data[result][1] + "\t" + config_name + "\n")
82 | 
83 |         print('.', end='', flush=True)
84 | 
85 | outfile.close()
86 | 
87 | print("Total Time: ", time.process_time())
88 | 


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/Swings.py:
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  1 | import pandas as pd
  2 | import collections
  3 | import copy
  4 | import csv
  5 | from Error import *
  6 | import numpy as np
  7 | 
  8 | import plotly.plotly  as py
  9 | import plotly.offline as offline
 10 | import plotly.graph_objs as go
 11 | from datetime import datetime
 12 | 
 13 | Pivot_Point = collections.namedtuple('Pivot_Point', ['data', 'row', 'pos'])
 14 | Swing_Line = collections.namedtuple('Swing_Line', ['date_time', 'pos', 'row'])
 15 | 
 16 | DT_FORMAT = "%Y-%m-%d %H:%M:%S"
 17 | class Swing_Generator:
 18 |     DEBUG = False
 19 | 
 20 |     def __init__(self, data_file, swing_file, config_data):
 21 |         self.swing_file = swing_file
 22 |         self.swing_writer = None
 23 |         self.data_file = data_file
 24 |         self.ref_column = "NA"
 25 |         self.ATR_period = 0
 26 |         self.time_factor = -1
 27 |         self.price_factor = 0
 28 |         self.update = False
 29 |         swing_column = None
 30 |         if self.DEBUG: print(self.OHLC_data.tail())
 31 | 
 32 |         self.ref_column = "Close" if config_data["reference_price"] == "Close" else "HighLow"
 33 |         self.swings_column_high = "Close" if config_data["reference_price"] == "Close" else "High"
 34 |         self.swings_column_low = "Close" if config_data["reference_price"] == "Close" else "Low"
 35 |         self.ATR_period = int(config_data["atr_period"])
 36 |         self.time_factor = int(config_data["time_factor"])
 37 |         self.price_factor = float(config_data["price_factor"])
 38 | 
 39 |         if self.DEBUG:
 40 |             print("Reference Column:", self.ref_column)
 41 |             print("ATR period:", self.ATR_period)
 42 |             print("Time Factor:", self.time_factor)
 43 |             print("Price Factor:", self.price_factor)
 44 | 
 45 | 
 46 | 
 47 |         if self.ref_column == "NA" or self.ATR_period == 0 or self.time_factor == -1 or self.price_factor == 0 or swing_column == "NA":
 48 |             raise ValueError("One or more required attributes not found in configuration file: " + configfile)
 49 | 
 50 |     def generate_swings(self):
 51 |         self.OHLC_data = pd.read_csv(self.data_file, names=['Date_Time', 'Open', 'High', 'Low', 'Close'])
 52 |         self.OHLC_data['Date_Time'] = pd.to_datetime(self.OHLC_data['Date_Time'], format=DT_FORMAT)
 53 |         self.OHLC_data = self.OHLC_data.set_index('Date_Time', drop=False)
 54 |         self.OHLC_data = self.Average_True_Range(self.OHLC_data, self.ATR_period)
 55 | 
 56 |         #Setup Stuff
 57 |         swing_file = open(self.swing_file, 'w', newline='')
 58 |         self.swing_writer = csv.writer(swing_file, delimiter=',')
 59 |         total_rows = len(self.OHLC_data.index)
 60 | 
 61 |         if total_rows < self.ATR_period:
 62 |             eprint("Not enough data to calculate ATR")
 63 |             return False
 64 | 
 65 |         row_count = 0
 66 |         reg_point = Pivot_Point(0,0,0)
 67 |         swing_point = Pivot_Point(0,0,0)
 68 |         temp_close_var = "Close"
 69 |         ####Find first Swing point####
 70 | 
 71 |         HH = (self.OHLC_data.iloc[0], 0)
 72 |         LL = (self.OHLC_data.iloc[0], 0)
 73 |         HH_ATR_Limit = HH[0][temp_close_var] - (HH[0]["ATR"]*self.price_factor)
 74 |         LL_ATR_Limit = LL[0][temp_close_var] + (LL[0]["ATR"]*self.price_factor)
 75 | 
 76 |         found_first_swing = False
 77 |         while not found_first_swing and row_count < total_rows:
 78 |             current_row = self.OHLC_data.iloc[row_count]
 79 | 
 80 |             if current_row[temp_close_var] >= LL_ATR_Limit and (row_count - self.time_factor) > LL[1]:
 81 |                 self.swing_writer.writerow([LL[0]['Date_Time'], LL[0][self.swings_column_low], "Low", LL[1]]) #write out first swing point
 82 |                 swing_point = copy.deepcopy(reg_point)
 83 |                 reg_point = Pivot_Point(current_row, row_count, "High")
 84 |                 found_first_swing = True
 85 |             elif current_row[temp_close_var] <= HH_ATR_Limit and (row_count - self.time_factor) > HH[1]:
 86 |                 self.swing_writer.writerow([HH[0]['Date_Time'], HH[0][self.swings_column_high], "High", HH[1]]) #write out first swing point
 87 |                 swing_point = copy.deepcopy(reg_point)
 88 |                 reg_point = Pivot_Point(current_row, row_count, "Low")
 89 |                 found_first_swing = True
 90 |             elif current_row[temp_close_var] < LL[0][temp_close_var]:
 91 |                 LL = (current_row, row_count)
 92 |                 LL_ATR_Limit = LL[0][temp_close_var] + (LL[0]["ATR"]*self.price_factor)
 93 |             elif current_row[temp_close_var] > HH[0][temp_close_var]:
 94 |                 HH = (current_row, row_count)
 95 |                 HH_ATR_Limit = HH[0][temp_close_var] - (HH[0]["ATR"]*self.price_factor)
 96 |             row_count += 1
 97 | 
 98 |         if not found_first_swing:
 99 |             eprint("Never found a swing")
100 |             return False
101 | 
102 |         if self.DEBUG:
103 |             print("First Registerd Point", reg_point)
104 |             print("Current Row Count:", row_count, "\t Total Rows:", total_rows)
105 | 
106 | 
107 |         #######Find all swings following first swing by looping through prices until finding new RP#######
108 |         self.calculate_remaining_swings(swing_point, reg_point, row_count, total_rows)
109 | 
110 |         swing_file.close()
111 | 
112 |         return True
113 | 
114 |     def update_swings(self):
115 |         swing_file = open(self.swing_file, 'r', newline='')
116 |         lines = swing_file.readlines()
117 |         swing_file.close()
118 | 
119 |         # Read in the last swing and last reg point
120 |         data_tup = lines[-2].split(',')
121 |         last_swing = Swing_Line(date_time=data_tup[0], pos=data_tup[2], row=int(data_tup[3]))
122 |         data_tup = lines[-1].split(',')
123 |         last_reg = Swing_Line(date_time=data_tup[0], pos=data_tup[2], row=int(data_tup[3]))
124 | 
125 |         # Read in only necessary OHLC data
126 |         self.OHLC_data = pd.read_csv(self.data_file, names=['Date_Time', 'Open', 'High', 'Low', 'Close'], skiprows=(last_swing.row - self.ATR_period))
127 |         self.OHLC_data['Date_Time'] = pd.to_datetime(self.OHLC_data['Date_Time'], format=DT_FORMAT)
128 |         self.OHLC_data = self.OHLC_data.set_index('Date_Time', drop=False)
129 |         self.OHLC_data = self.Average_True_Range(self.OHLC_data, self.ATR_period)
130 | 
131 |         # Set swing_point and reg_point
132 |         datetime_swing = datetime.strptime(last_swing.date_time, DT_FORMAT)
133 |         swing_point = Pivot_Point(self.OHLC_data.loc[datetime_swing], int(last_swing.row), last_swing.pos)
134 |         datetime_reg = datetime.strptime(last_reg.date_time, DT_FORMAT)
135 |         reg_point = Pivot_Point(self.OHLC_data.loc[datetime_reg], int(last_reg.row), last_reg.pos)
136 | 
137 |         # Check for any update at all.
138 |         if (self.OHLC_data.loc[datetime_reg]["Date_Time"]) == (self.OHLC_data.tail(1).iloc[0]["Date_Time"]):
139 |             return
140 |         #Set up all vars for remaning swings calculation
141 |         row_count = reg_point.row + 1
142 |         total_rows = row_count + len(self.OHLC_data.index) - 1
143 |         swing_file = open(self.swing_file, 'a', newline='')
144 |         self.swing_writer = csv.writer(swing_file, delimiter=',')
145 | 
146 |         self.calculate_remaining_swings(swing_point, reg_point, row_count, total_rows)
147 | 
148 |         swing_file.close()
149 | 
150 |     def calculate_remaining_swings(self, swing_point, reg_point, row_count, total_rows):
151 | 
152 |         ref_column_low = self.ref_column if self.ref_column == "Close" else "Low"
153 |         ref_column_high = self.ref_column if self.ref_column == "Close" else "High"
154 | 
155 | 
156 |         first_reg_date = reg_point.data["Date_Time"]
157 |         my_OHLC_data = self.OHLC_data.loc[first_reg_date:].iloc[1:]
158 | 
159 |         for index, current_row in my_OHLC_data.iterrows():
160 |             if reg_point.pos == "High":
161 |                 violation_price = reg_point.data[ref_column_high] - (reg_point.data["ATR"]*self.price_factor)
162 |                 if current_row[ref_column_high] > reg_point.data[ref_column_high]: #new extreme with direction
163 |                     reg_point = reg_point._replace(data = current_row, row = row_count)
164 |                 elif current_row[ref_column_low] < violation_price and (row_count - self.time_factor) > reg_point.row: #Violated ATR range in opposite direction
165 |                     if self.DEBUG:
166 |                         print("Violated ATR in the Low direction. Register a new Low, write out previous RP High as Swing High")
167 |                         print("Previous REgisted Point: ", reg_point)
168 | 
169 |                     self.swing_writer.writerow([reg_point.data["Date_Time"], reg_point.data[self.swings_column_high if reg_point.pos == "High" else self.swings_column_low], reg_point.pos, reg_point.row]) #write out previous RP as SP
170 |                     swing_point = copy.deepcopy(reg_point)
171 |                     reg_point = Pivot_Point(current_row, row_count, "Low") #re-regsiter RP
172 | 
173 |                     if self.DEBUG:
174 |                         print("New Registed Point: ", reg_point)
175 | 
176 |             elif reg_point.pos == "Low":
177 |                 violation_price = reg_point.data[ref_column_low] + (reg_point.data["ATR"]*self.price_factor)
178 | 
179 |                 if current_row[ref_column_low] < reg_point.data[ref_column_low]: #new extreme with direction
180 |                     reg_point = reg_point._replace(data = current_row, row = row_count)
181 |                 elif current_row[ref_column_high] > violation_price and (row_count - self.time_factor) > reg_point.row: #Violated ATR range in opposite direction
182 |                     if self.DEBUG:
183 |                         print("Violated ATR in the High direction. Register a new High, write out previous RP low as Swing low")
184 |                         print("Previous REgisted Point: ", reg_point)
185 | 
186 |                     self.swing_writer.writerow([reg_point.data["Date_Time"], reg_point.data[self.swings_column_high if reg_point.pos == "High" else self.swings_column_low], reg_point.pos, reg_point.row]) #write out previous RP as SP
187 |                     swing_point = copy.deepcopy(reg_point)
188 |                     reg_point = Pivot_Point(current_row, row_count, "High") #re-regsiter RP
189 | 
190 |                     if self.DEBUG:
191 |                         print("New Registed Point: ", reg_point)
192 |             else:
193 |                 eprint("Registered point posistion is something other than \"High\" or \"Low\"")
194 |                 return False
195 | 
196 |             row_count += 1
197 | 
198 | 
199 |         if not first_reg_date == reg_point.data["Date_Time"]:
200 |             self.swing_writer.writerow([reg_point.data["Date_Time"], reg_point.data[self.swings_column_high if reg_point.pos == "High" else self.swings_column_low], reg_point.pos, reg_point.row]) #set last RP as a SP
201 | 
202 |         ###############################################################################################################
203 | 
204 |     def reverse_file(self):
205 |         swing_file = open(self.swing_file, 'r')
206 |         reversed_lines = []
207 |         for line in reversed(swing_file.readlines()):
208 |             reversed_lines.append(line)
209 |         swing_file.close()
210 | 
211 |         swing_file = open(self.swing_file, 'w')
212 |         for line in reversed_lines:
213 |             swing_file.write(line)
214 |         swing_file.close()
215 | 
216 |     def Average_True_Range(self, df, n):
217 |         """
218 |         :param df: pandas.DataFrame
219 |         :param n:
220 |         :return: pandas.DataFrame
221 |         """
222 |         i = 1
223 |         TR_l = [df.iloc[0]['High'] - df.iloc[0]['Low']]
224 |         rows = len(df.index)
225 |         while i < rows:
226 |             TR = max(df.iloc[i]['High'] - df.iloc[i]['Low'], abs(df.iloc[i]['High'] - df.iloc[i-1]['Close']), abs(df.iloc[i]['Low'] - df.iloc[i-1]['Close']))
227 |             TR_l.append(TR)
228 |             i += 1
229 |         ATR_df = pd.DataFrame({'col':TR_l})
230 |         ATR_l = []
231 |         for i in range(len(TR_l)):
232 |             if i < n:
233 |                 if i == (n - 1):
234 |                     ATR_l.append(np.mean(TR_l[:n]))
235 |                 else:
236 |                     ATR_l.append(0)
237 |             else:
238 |                 ATR_l.append((ATR_l[i - 1] * (n - 1) + TR_l[i]) / n)
239 | 
240 |         return df.assign(ATR=ATR_l)
241 | 
242 |     def graph_OHLC(self):
243 |         #not quite there, but the other one works, which is what i really care about
244 |         OHLC_trace = go.Ohlc(x=self.OHLC_data.Date_Time,
245 |                 open=self.OHLC_data.Open,
246 |                 high=self.OHLC_data.High,
247 |                 low=self.OHLC_data.Low,
248 |                 close=self.OHLC_data.Close,
249 |                 name="OHLC Data",
250 |                 increasing=dict(line=dict(color= '#408e4a')),
251 |                 decreasing=dict(line=dict(color= '#cc2718')))
252 | 
253 |         swing_data = pd.read_csv(self.swing_file, names=['Date_Time', 'Price', 'Direction', 'Row'], parse_dates=True)
254 |         swing_trace = go.Scatter(
255 |             x = swing_data.Date_Time,
256 |             y = swing_data.Price,
257 |             mode = 'lines+markers',
258 |             name = 'Swings',
259 |             line = dict(
260 |                 color = ('rgb(111, 126, 130)'),
261 |                 width = 3)
262 |         )
263 | 
264 |         data = [OHLC_trace, swing_trace]
265 |         layout = go.Layout(xaxis = dict(rangeslider = dict(visible = False)), title= self.data_file[:-4])
266 | 
267 |         fig = go.Figure(data=data, layout=layout)
268 |         py.plot(fig, filename=self.data_file + ".html", output_type='file')
269 | 
270 |     def export_OHLC_graph(self):
271 | 
272 |         if self.update:
273 |             print("Did update, graph is screwy")
274 |         OHLC_trace = go.Ohlc(x=self.OHLC_data.Date_Time,
275 |                 open=self.OHLC_data.Open,
276 |                 high=self.OHLC_data.High,
277 |                 low=self.OHLC_data.Low,
278 |                 close=self.OHLC_data.Close,
279 |                 name="OHLC Data",
280 |                 increasing=dict(line=dict(color= '#408e4a')),
281 |                 decreasing=dict(line=dict(color= '#cc2718')))
282 | 
283 |         swing_data = pd.read_csv(self.swing_file, names=['Date_Time', 'Price', 'Direction', 'Row'], parse_dates=True)
284 |         swing_trace = go.Scatter(
285 |             x = swing_data.Date_Time,
286 |             y = swing_data.Price,
287 |             mode = 'lines+markers',
288 |             name = 'Swings',
289 |             line = dict(
290 |                 color = ('rgb(111, 126, 130)'),
291 |                 width = 3)
292 |         )
293 | 
294 |         data = [OHLC_trace, swing_trace]
295 | 
296 |         layout = {
297 |             'title': self.data_file[:-4],
298 |             'yaxis': {'title': 'Price'},
299 |         }
300 |         fig = go.Figure(data=data, layout=layout)
301 |         offline.plot(fig, output_type='file',filename=self.data_file + ".html", image='png', image_filename=self.data_file)
302 | 


--------------------------------------------------------------------------------
/ElliotAnalyzer.py:
--------------------------------------------------------------------------------
  1 | import pandas as pd
  2 | from Error import *
  3 | import datetime as dt
  4 | import copy
  5 | import configparser
  6 | 
  7 | import plotly.plotly  as py
  8 | import plotly.offline as offline
  9 | import plotly.graph_objs as go
 10 | DT_FORMAT = "%Y-%m-%d %H:%M:%S"
 11 | #TODO: change wave data to be a list of datas when adding support for more data patterns
 12 | 
 13 | class Elliot_Analyzer:
 14 |     DEBUG = False
 15 | 
 16 |     def __init__(self, currency_name, swing_file, OHLC_data_file, config_file="AnalyzerConfig.conf"):
 17 |         #read in configFile
 18 |         self.config = configparser.ConfigParser()
 19 |         self.config.read(config_file)
 20 | 
 21 |         self.swing_data = pd.read_csv(swing_file, names=['Date_Time', 'Price', 'Pos', 'Row']).tail(9)
 22 |         self.swing_data['Date_Time'] = pd.to_datetime(self.swing_data['Date_Time'], format=DT_FORMAT)
 23 | 
 24 |         if self.DEBUG: print("All the Swing Data")
 25 |         if self.DEBUG: print(self.swing_data)
 26 |         last_swing_row = self.swing_data.iloc[0]["Row"]
 27 |         self.OHLC_data = pd.read_csv(OHLC_data_file, names=['Date_Time', 'Open', 'High', 'Low', 'Close'], skiprows=last_swing_row)
 28 |         self.OHLC_data['Date_Time'] = pd.to_datetime(self.OHLC_data['Date_Time'], format=DT_FORMAT)
 29 |         self.OHLC_data = self.OHLC_data.set_index('Date_Time')
 30 | 
 31 |         # self.OHLC_data = self.OHLC_data.truncate(before=self.swing_data.iloc[0]['Date_Time'])
 32 |         if self.DEBUG: print("OHLCDATA")
 33 |         if self.DEBUG: print(self.OHLC_data)
 34 |         self.currency_name = currency_name
 35 |         self.wave_data = {}
 36 | 
 37 |     def analyze(self):
 38 |         # Set up Analysis type
 39 |         my_config = self.config_section_map(self.config, "Analysis_Type")
 40 | 
 41 |         if len(self.swing_data.index) < 6:
 42 |             eprint("Not enough swing data to do Wave 5 Analysis!")
 43 |             return []
 44 | 
 45 |         analysis_summary = []
 46 | 
 47 |         #Wave 5 analysis
 48 |         if my_config["wave5"] == "1" and self.wave5(self.swing_data.tail(6)):
 49 |             analysis_summary.append("Wave5")
 50 |         elif my_config["wave4"] == "1" and self.wave4(self.swing_data.tail(5)):
 51 |             analysis_summary.append("Wave4")
 52 |         elif my_config["wave3"] == "1" and self.wave3(self.swing_data.tail(4)):
 53 |             analysis_summary.append("Wave3")
 54 |         elif my_config["wave2"] == "1" and self.wave2(self.swing_data.tail(3)):
 55 |             analysis_summary.append("Wave2")
 56 | 
 57 |         #wave c
 58 |         if my_config["wavec"] == "1"  and len(self.swing_data.index) < 9:
 59 |             eprint("Not enough swing data to do Wave C Analysis!")
 60 |             return []
 61 | 
 62 |         if my_config["wavec"] == "1" and self.waveC(self.swing_data.tail(9)):
 63 |             analysis_summary.append("WaveC")
 64 | 
 65 |         # if self.gartley(self.swing_data.tail(4)) something like this for other analysis
 66 |         return analysis_summary
 67 | 
 68 |     def wave2(self, swings, downward_call=False):
 69 | 
 70 |         relevant_swings = swings
 71 |         my_config = self.config_section_map(self.config, "Wave2")
 72 |         wave1_swings = (relevant_swings.iloc[0]['Price'], relevant_swings.iloc[1]['Price'])
 73 |         wave2_price = relevant_swings.iloc[2]["Price"]
 74 | 
 75 |         wave1_inrets = self.fib_retracement(wave1_swings[0], wave1_swings[1], list(my_config.values()))
 76 | 
 77 |         #Ensure Wave 2 has not gone past start of wave 1
 78 |         violated = True
 79 |         if relevant_swings.iloc[2]["Pos"] == "Low":
 80 |             violated = wave2_price < self.OHLC_data.loc[relevant_swings.iloc[0]['Date_Time']]["Close"]
 81 |         else:
 82 |             violated = wave2_price > self.OHLC_data.loc[relevant_swings.iloc[0]['Date_Time']]["Close"]
 83 | 
 84 |         #check for minimum requirements first
 85 |         wave_min = self.in_range(wave2_price, wave1_inrets[my_config['inret_wave1_min']], wave1_inrets[my_config['inret_wave1_max']]) and not violated
 86 |         if wave_min:
 87 |             wave_typ = self.in_range(wave2_price, wave1_inrets[my_config['inret_wave1_typical_min']], wave1_inrets[my_config['inret_wave1_typical_max']])
 88 |             if not downward_call:
 89 |                 if wave_typ:
 90 |                     self.wave_data["Wave2"] = (relevant_swings, "Typical")
 91 |                 else:
 92 |                     self.wave_data["Wave2"] = (relevant_swings, "Minimum")
 93 | 
 94 |         return wave_min
 95 | 
 96 |     def wave3(self, swings, downward_call=False):
 97 |         #check for wave 2 requirements
 98 |         if not self.wave2(swings.head(3), True):
 99 |             if self.DEBUG: print("Failed Wave2 on downward call")
100 |             return False
101 | 
102 |         relevant_swings = swings
103 |         my_config = self.config_section_map(self.config, "Wave3")
104 | 
105 |         wave1_swings = (relevant_swings.iloc[0]['Price'], relevant_swings.iloc[1]['Price'])
106 |         wave2_swings = (relevant_swings.iloc[1]['Price'], relevant_swings.iloc[2]['Price'])
107 |         wave3_price = relevant_swings.iloc[3]["Price"]
108 |         wave2_price = relevant_swings.iloc[2]["Price"]
109 | 
110 |         wave1_app_levels = [level for option,level in my_config.items() if option.startswith('app')]
111 |         wave2_exret_levels = [level for option,level in my_config.items() if option.startswith('exret')]
112 |         wave1_apps = self.fib_projection(wave1_swings[0], wave1_swings[1], wave2_price, wave1_app_levels)
113 |         wave2_exrets = self.fib_retracement(wave2_swings[0], wave2_swings[1], wave2_exret_levels)
114 |         combo = {**wave1_apps, **wave2_exrets}
115 | 
116 |         #check for minimum requirements first, then typicaL
117 |         wave_min = self.in_range(wave3_price, combo[min(combo, key=combo.get)], combo[max(combo, key=combo.get)])
118 |         if wave_min:
119 |             wave_typ = self.in_range(wave3_price, wave1_apps[my_config['app_wave1_typical']], wave2_exrets[my_config['exret_wave2_typical']])
120 |             if not downward_call:
121 |                 if wave_typ:
122 |                     self.wave_data["Wave3"] = (relevant_swings, "Typical")
123 |                 else:
124 |                     self.wave_data["Wave3"] = (relevant_swings, "Minimum")
125 | 
126 |         return wave_min
127 | 
128 |     def wave4(self, swings, downward_call=False):
129 |         #check for wave 3 requirements
130 |         if not self.wave3(swings.head(4), True):
131 |             if self.DEBUG: print("Failed Wave3 on downward call")
132 |             return False
133 | 
134 |         relevant_swings = swings
135 |         my_config = self.config_section_map(self.config, "Wave4")
136 | 
137 |         wave1_swings = (relevant_swings.iloc[0]['Price'], relevant_swings.iloc[1]['Price'])
138 |         wave3_swings = (relevant_swings.iloc[2]['Price'], relevant_swings.iloc[3]['Price'])
139 | 
140 |         wave4_price = relevant_swings.iloc[4]["Price"]
141 | 
142 |         wave3_ret_levels = [level for option,level in my_config.items() if option.startswith('ret_wave3')]
143 |         wave1_3_ret_levels = [level for option,level in my_config.items() if option.startswith('ret_wave1_3')]
144 | 
145 | 
146 |         wave3_rets = self.fib_retracement(wave3_swings[0], wave3_swings[1], wave3_ret_levels)
147 |         wave1_3_rets = self.fib_retracement(wave1_swings[0], wave3_swings[1], wave1_3_ret_levels)
148 |         combo = {**wave3_rets, **wave1_3_rets}
149 | 
150 |         #Ensure Wave 4 has not gone into closing range of wave 1
151 |         violated = True
152 |         if relevant_swings.iloc[4]["Pos"] == "Low":
153 |             violated = wave4_price < self.OHLC_data.loc[relevant_swings.iloc[1]['Date_Time']]["Close"]
154 |         else:
155 |             violated = wave4_price > self.OHLC_data.loc[relevant_swings.iloc[1]['Date_Time']]["Close"]
156 | 
157 |         #check for minimum requirements first, then typicaL
158 |         wave_min = self.in_range(wave4_price, combo[min(combo, key=combo.get)], combo[max(combo, key=combo.get)]) and not violated
159 | 
160 |         if wave_min:
161 |             wave_typ = self.in_range(wave4_price, wave3_rets[my_config['ret_wave3_min']], wave3_rets[my_config['ret_wave3_typical']])
162 |             if not downward_call:
163 |                 if wave_typ:
164 |                     self.wave_data["Wave4"] = (relevant_swings, "Typical")
165 |                 else:
166 |                     self.wave_data["Wave4"] = (relevant_swings, "Minimum")
167 | 
168 |         return wave_min
169 | 
170 |     def wave5(self, swings):
171 |         if not self.wave4(swings.head(5), True):
172 |             if self.DEBUG : print("Failed Wave4 on downward call")
173 |             return False
174 | 
175 |         relevant_swings = swings
176 |         my_config = self.config_section_map(self.config, "Wave5")
177 | 
178 |         wave1_swings = (relevant_swings.iloc[0]['Price'], relevant_swings.iloc[1]['Price'])
179 |         wave3_swings = (relevant_swings.iloc[2]['Price'], relevant_swings.iloc[3]['Price'])
180 |         wave4_swings = (relevant_swings.iloc[3]['Price'], relevant_swings.iloc[4]['Price'])
181 |         wave5_swings = (relevant_swings.iloc[4]['Price'], relevant_swings.iloc[5]['Price'])
182 | 
183 |         wave4_price = relevant_swings.iloc[4]["Price"]
184 |         wave5_price = relevant_swings.iloc[5]["Price"]
185 | 
186 |         wave1_3_app_levels = [level for option,level in my_config.items() if option.startswith('app_wave1_3')]
187 |         wave1_app_levels = [level for option,level in my_config.items() if option.startswith('app_wave1')]
188 |         wave4_exret_levels = [level for option,level in my_config.items() if option.startswith('exret_wave4')]
189 | 
190 |         wave1_3_apps = self.fib_projection(wave1_swings[0], wave3_swings[1], wave4_price, wave1_3_app_levels)
191 |         wave1_apps = self.fib_projection(wave1_swings[0], wave1_swings[1], wave4_price, wave1_app_levels)
192 |         wave4_exrets = self.fib_retracement(wave4_swings[0], wave4_swings[1], wave4_exret_levels)
193 |         combo = {**wave1_3_apps, **wave1_apps, **wave4_exrets}
194 | 
195 |         # Ensure wave 3 is not shortest wave of 1,3 and 5
196 |         violated = False
197 |         wave1_magnitude = abs(wave1_swings[0] - wave1_swings[1])
198 |         wave3_magnitude = abs(wave3_swings[0] - wave3_swings[1])
199 |         wave5_magnitude = abs(wave5_swings[0] - wave5_swings[1])
200 | 
201 |         if (wave3_magnitude < wave1_magnitude) and (wave3_magnitude < wave5_magnitude):
202 |             violated = True
203 | 
204 |         wave_min = not violated
205 |         if relevant_swings.iloc[5]["Pos"] == "High":
206 |             wave_min = wave_min and wave5_price > combo[min(combo, key=combo.get)]
207 |         else:
208 |             wave_min = wave_min and wave5_price <  combo[max(combo, key=combo.get)]
209 | 
210 |         if wave_min:
211 |             price_in_wave1_3 = self.in_range(wave5_price, wave1_3_apps[my_config['app_wave1_3_min']], wave1_3_apps[my_config['app_wave1_3_typical']])
212 |             wave1_in_wave1_3 = self.in_range(wave1_apps[my_config["app_wave_1"]], wave1_3_apps[my_config['app_wave1_3_min']], wave1_3_apps[my_config['app_wave1_3_typical']])
213 |             price_in_wave_4 = self.in_range(wave5_price, wave4_exrets[my_config['exret_wave4_min']], wave4_exrets[my_config['exret_wave4_typical']])
214 |             wave1_in_wave4 = self.in_range(wave1_apps[my_config["app_wave_1"]], wave4_exrets[my_config['exret_wave4_min']], wave4_exrets[my_config['exret_wave4_typical']])
215 |             wave_high_prob = price_in_wave1_3 and price_in_wave_4 and wave1_in_wave1_3 and wave1_in_wave4
216 | 
217 |             if wave_high_prob:
218 |                 self.wave_data["Wave5"] = (relevant_swings, "HighProbability")
219 |             else:
220 |                 wave_typ = (price_in_wave1_3 and wave1_in_wave1_3) or (price_in_wave1_3 and price_in_wave_4) or (price_in_wave_4 and wave1_in_wave4)
221 |                 if wave_typ:
222 |                     self.wave_data["Wave5"] = (relevant_swings, "Typical")
223 |                 else:
224 |                     self.wave_data["Wave5"] = (relevant_swings, "Minimum")
225 | 
226 |         return wave_min
227 | 
228 |     def waveC(self, swings):
229 |         relevant_swings = swings.tail(4)
230 |         my_config = self.config_section_map(self.config, "WaveC")
231 | 
232 |         #Ensure corrective pattern is following some kind of trend
233 |         following_trend, trend_swings = self.trending(swings.head(6), my_config["proper_trend"], my_config["mini_trend"], my_config["little_trend"])
234 |         if not following_trend: return False
235 | 
236 |         waveA_swings = (relevant_swings.iloc[0]['Price'], relevant_swings.iloc[1]['Price'])
237 |         waveB_swings = (relevant_swings.iloc[1]['Price'], relevant_swings.iloc[2]['Price'])
238 |         waveB_price = relevant_swings.iloc[2]['Price']
239 |         waveC_price = relevant_swings.iloc[3]['Price']
240 | 
241 | 
242 |         prior_trend_levels = [level for option,level in my_config.items() if option.startswith('inret_prior_trend')]
243 |         waveA_app_levels = [level for option,level in my_config.items() if option.startswith('app_wavea')]
244 |         waveB_exret_levels = [level for option,level in my_config.items() if option.startswith('exret_waveb')]
245 | 
246 |         prior_trend_rets = self.fib_retracement(trend_swings[0], trend_swings[1], prior_trend_levels)
247 |         wavea_apps = self.fib_projection(waveA_swings[0], waveA_swings[1], waveB_price, waveA_app_levels)
248 |         waveb_exrets = self.fib_retracement(waveB_swings[0], waveB_swings[1], waveB_exret_levels)
249 |         combo = {**wavea_apps, **waveb_exrets}
250 | 
251 | 
252 |         wave_min = self.in_range(waveC_price, prior_trend_rets[my_config['inret_prior_trend_min']], prior_trend_rets[my_config['inret_prior_trend_max']])
253 |         wave_min = wave_min and self.in_range(waveC_price, combo[min(combo, key=combo.get)], combo[max(combo, key=combo.get)])
254 |         #Ensure Wave B does not trade past start of wave A
255 |         #Ensure Wave C Trades past Wave A
256 |         if relevant_swings.iloc[0]["Pos"] == "High":
257 |             wave_min = wave_min and self.OHLC_data.loc[relevant_swings.iloc[2]['Date_Time']]["High"] < self.OHLC_data.loc[relevant_swings.iloc[0]['Date_Time']]["Low"]
258 |             wave_min = wave_min and self.OHLC_data.loc[relevant_swings.iloc[3]['Date_Time']]["Close"] < relevant_swings.iloc[1]['Price']
259 |         else:
260 |             wave_min = wave_min and self.OHLC_data.loc[relevant_swings.iloc[2]['Date_Time']]["High"] > self.OHLC_data.loc[relevant_swings.iloc[0]['Date_Time']]["Low"]
261 |             wave_min = wave_min and self.OHLC_data.loc[relevant_swings.iloc[3]['Date_Time']]["Close"] > relevant_swings.iloc[1]['Price']
262 | 
263 |         if wave_min:
264 |             wave_typ = self.in_range(waveC_price, prior_trend_rets[my_config['inret_prior_trend_typical_min']], prior_trend_rets[my_config['inret_prior_trend_typical_max']])
265 |             if wave_typ: self.wave_data["WaveC"] = (swings, "Typical")
266 |             else: self.wave_data["WaveC"] = (swings, "Minimum")
267 | 
268 |         return wave_min
269 | 
270 |     def trending(self, swings, proper, mini, little):
271 |         mini_trend = False
272 |         little_trend = False
273 |         if mini == "1":
274 |             mini_trend = True
275 | 
276 |         if little == "1":
277 |             little_trend = True
278 | 
279 |         swing_5_3 = False
280 |         swing_3_1 = False
281 |         swing_4_2 = False
282 |         swing_2_0 = False
283 | 
284 |         if swings.iloc[0]['Pos'] == "Low":
285 |             swing_5_3 = swings.iloc[5]['Price'] > swings.iloc[3]['Price']
286 |             swing_3_1 = swings.iloc[3]['Price'] > swings.iloc[1]['Price']
287 |             swing_4_2 = swings.iloc[4]['Price'] > swings.iloc[2]['Price']
288 |             swing_2_0 = swings.iloc[2]['Price'] > swings.iloc[0]['Price']
289 |             swing_4_1 = swings.iloc[4]['Price'] > swings.iloc[1]['Price']
290 |         else:
291 |             swing_5_3 = swings.iloc[5]['Price'] < swings.iloc[3]['Price']
292 |             swing_3_1 = swings.iloc[3]['Price'] < swings.iloc[1]['Price']
293 |             swing_4_2 = swings.iloc[4]['Price'] < swings.iloc[2]['Price']
294 |             swing_2_0 = swings.iloc[2]['Price'] < swings.iloc[0]['Price']
295 |             swing_4_1 = swings.iloc[4]['Price'] < swings.iloc[1]['Price']
296 | 
297 | 
298 |         little_trend = little_trend and swing_5_3 and swing_4_2
299 |         big_trend = little_trend and swing_3_1 and swing_2_0
300 | 
301 |         if proper == "1":
302 |             big_trend = big_trend and swing_4_1
303 | 
304 |         swing_1 = swings.iloc[4]['Price']
305 |         swing_2 = swings.iloc[5]['Price']
306 |         if big_trend:
307 |             swing_1 = swings.iloc[0]['Price']
308 |         elif little_trend:
309 |             swing_1 = swings.iloc[2]['Price']
310 | 
311 |         return big_trend or little_trend or mini_trend, (swing_1, swing_2)
312 | 
313 |     def fib_retracement(self, swing_1, swing_2, fib_levels):
314 |         if self.DEBUG: print("Swing1: ", swing_1, "\nSwing2: ", swing_2)
315 | 
316 |         wave_length = abs(swing_1 - swing_2)
317 | 
318 |         fib_retracements = []
319 |         for level in fib_levels:
320 |             fib_retracements.append(swing_2 + (-(wave_length * float(level)) if swing_1 < swing_2 else (wave_length * float(level))))
321 | 
322 |         if self.DEBUG: print("Fib dictionary returned:\n", dict(zip(fib_levels, fib_retracements)))
323 | 
324 |         return dict(zip(fib_levels, fib_retracements)) # Return a dictionary of fib levels mapped to their retracments
325 | 
326 |     def fib_projection(self, swing_1, swing_2, projection_point, fib_levels):
327 |         if self.DEBUG: print("Swing1: ", swing_1, "\nSwing2: ", swing_2, "\nProjection Point: ", projection_point)
328 | 
329 |         wave_length = abs(swing_1 - swing_2)
330 | 
331 |         fib_projections = []
332 |         for level in fib_levels:
333 |             fib_projections.append(projection_point + (-(wave_length * float(level)) if swing_1 > swing_2 else (wave_length * float(level))))
334 | 
335 |         if self.DEBUG: print("Fib dictionary returned:\n", dict(zip(fib_levels, fib_projections)))
336 | 
337 |         return dict(zip(fib_levels, fib_projections)) # Return a dictionary of fib levels mapped to their retracments
338 | 
339 |     def in_range(self, x, range_1, range_2):
340 |         bottom = range_1 if range_1 < range_2 else range_2
341 |         top = range_1 if range_1 > range_2 else range_2
342 | 
343 |         return bottom <= x <= top
344 | 
345 |     def export_graphs(self, output_path):
346 |         for key,value in self.wave_data.items():
347 |             my_swing_data = value[0]
348 |             print("My Swing Data:", my_swing_data)
349 | 
350 |             lables = []
351 |             if key == "WaveC":
352 |                 lables = ["","","","","","","A", "B", "C"]
353 |             else:
354 |                 lables = [str(x) for x in range(len(my_swing_data.index))]
355 | 
356 |             OHLC_trace = go.Ohlc(x=self.OHLC_data.index,
357 |                     open=self.OHLC_data.Open,
358 |                     high=self.OHLC_data.High,
359 |                     low=self.OHLC_data.Low,
360 |                     close=self.OHLC_data.Close,
361 |                     name="OHLC Data",
362 |                     increasing=dict(line=dict(color= '#408e4a')),
363 |                     decreasing=dict(line=dict(color= '#cc2718')))
364 | 
365 |             print([str(x) for x in range(1, len(my_swing_data.index))])
366 |             swing_trace = go.Scatter(
367 |                 x = my_swing_data.Date_Time,
368 |                 y = my_swing_data.Price,
369 |                 mode = 'lines+markers+text',
370 |                 name = 'Swings',
371 |                 line = dict(
372 |                     color = ('rgb(111, 126, 130)'),
373 |                     width = 3),
374 |                 text=lables,
375 |                 textposition='top center',
376 |                 textfont=dict(
377 |                     family='sans serif',
378 |                     size=35,
379 |                     color='#2c3035'
380 |                 )
381 |             )
382 | 
383 |             data = [OHLC_trace, swing_trace]
384 | 
385 |             time_frame = output_path.split("_")[-1]
386 | 
387 |             layout = dict(
388 |                             title=self.currency_name + " " + key + " " + time_frame + ": " + value[1],
389 |                             xaxis = dict(
390 |                             type="category"))
391 | 
392 |             fig = go.Figure(data=data, layout=layout)
393 |             # offline.plot(fig, output_type='file',filename=self.currency_name + ".html", image='png', image_filename=self.currency_name)
394 |             offline.plot(fig, output_type='file',filename=output_path + "_" + key + ".html", auto_open=False)
395 | 
396 |     def config_section_map(self, config, section):
397 |         dict1 = {}
398 |         options = config.options(section)
399 |         for option in options:
400 |             try:
401 |                 dict1[option] = config.get(section, option)
402 |                 if dict1[option] == -1:
403 |                     print("skip: %s" % option)
404 |             except:
405 |                 print("exception on %s!" % option)
406 |                 dict1[option] = None
407 |         return dict1
408 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 | 
513 |   If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 | 
521 |   A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License.  You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 | 
536 |   Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 | 
540 |   12. No Surrender of Others' Freedom.
541 | 
542 |   If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License.  If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all.  For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 | 
552 |   13. Use with the GNU Affero General Public License.
553 | 
554 |   Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work.  The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 | 
563 |   14. Revised Versions of this License.
564 | 
565 |   The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time.  Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 | 
570 |   Each version is given a distinguishing version number.  If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation.  If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 | 
579 |   If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 | 
584 |   Later license versions may give you additional or different
585 | permissions.  However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 | 
589 |   15. Disclaimer of Warranty.
590 | 
591 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 | 
600 |   16. Limitation of Liability.
601 | 
602 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 | 
612 |   17. Interpretation of Sections 15 and 16.
613 | 
614 |   If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 | 
621 |                      END OF TERMS AND CONDITIONS
622 | 
623 |             How to Apply These Terms to Your New Programs
624 | 
625 |   If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 | 
629 |   To do so, attach the following notices to the program.  It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 | 
634 |     <one line to give the program's name and a brief idea of what it does.>
635 |     Copyright (C) <year>  <name of author>
636 | 
637 |     This program is free software: you can redistribute it and/or modify
638 |     it under the terms of the GNU General Public License as published by
639 |     the Free Software Foundation, either version 3 of the License, or
640 |     (at your option) any later version.
641 | 
642 |     This program is distributed in the hope that it will be useful,
643 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
644 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
645 |     GNU General Public License for more details.
646 | 
647 |     You should have received a copy of the GNU General Public License
648 |     along with this program.  If not, see <http://www.gnu.org/licenses/>.
649 | 
650 | Also add information on how to contact you by electronic and paper mail.
651 | 
652 |   If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 | 
655 |     <program>  Copyright (C) <year>  <name of author>
656 |     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 |     This is free software, and you are welcome to redistribute it
658 |     under certain conditions; type `show c' for details.
659 | 
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License.  Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 | 
664 |   You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | <http://www.gnu.org/licenses/>.
668 | 
669 |   The GNU General Public License does not permit incorporating your program
670 | into proprietary programs.  If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library.  If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License.  But first, please read
674 | <http://www.gnu.org/philosophy/why-not-lgpl.html>.
675 | 


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