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DanielGoldfarbDanielGoldfarb
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Merge pull request #309 from KenanHArik/master
Added the ability to create hollow-and-filled candle plots
2 parents 0b7b663 + 511019c commit f81a205

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‎.gitignore

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*.egg-info
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.tox/
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.cache/
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*.code-workspace

‎examples/data/yahoofinance-SPY-20200901-20210113.csv

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Date,Open,High,Low,Close,Adj Close,Volume
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2020-09-01,350.209991,352.709991,349.239990,352.600006,349.703522,54999300
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2020-09-02,354.670013,358.750000,353.429993,357.700012,354.761627,69540000
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2020-09-03,355.869995,356.380005,342.589996,345.390015,342.552765,148011100
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2020-09-04,346.130005,347.829987,334.869995,342.570007,339.755890,139156300
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2020-09-08,336.709991,342.640015,332.880005,333.209991,330.472778,114465300
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2020-09-09,337.549988,342.459991,336.609985,339.790009,336.998749,91462300
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2020-09-10,341.820007,342.529999,332.850006,333.890015,331.147217,90569500
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2020-09-11,335.820007,336.970001,331.000000,334.059998,331.315796,84680200
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2020-09-14,337.489990,340.380005,334.220001,338.459991,335.679657,65605700
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2020-09-15,341.119995,342.019989,338.470001,340.170013,337.375641,52920900
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2020-09-16,341.510010,343.059998,338.519989,338.820007,336.036713,82096000
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2020-09-17,333.559998,337.700012,332.989990,335.839996,333.081207,91523300
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2020-09-18,335.369995,335.489990,327.970001,330.649994,329.246521,105877900
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2020-09-21,325.700012,327.130005,321.730011,326.970001,325.582153,99450800
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2020-09-22,328.570007,330.899994,325.859985,330.299988,328.898010,63612100
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2020-09-23,330.899994,331.200012,322.100006,322.640015,321.270538,93112200
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2020-09-24,321.220001,326.799988,319.799988,323.500000,322.126892,76681300
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2020-09-25,322.579987,329.579987,321.640015,328.730011,327.334686,71069400
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2020-09-28,333.220001,334.959991,332.149994,334.190002,332.771515,64584600
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2020-09-29,333.970001,334.769989,331.619995,332.369995,330.959229,51304000
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2020-09-30,333.089996,338.290009,332.880005,334.890015,333.468567,103653800
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2020-10-01,337.690002,338.739990,335.010010,337.040009,335.609406,88698700
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2020-10-02,331.700012,337.010010,331.190002,333.839996,332.423004,89431100
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2020-10-05,336.059998,339.959991,336.010010,339.760010,338.317871,45713100
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2020-10-06,339.910004,342.170013,334.380005,334.929993,333.508362,90128900
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2020-10-07,338.119995,341.630005,338.089996,340.760010,339.313629,56999600
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2020-10-08,342.850006,343.850006,341.859985,343.779999,342.320801,45242500
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2020-10-09,345.559998,347.350006,344.890015,346.850006,345.377777,59528600
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2020-10-12,349.589996,354.019989,349.059998,352.429993,350.934082,80388500
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2020-10-13,352.279999,352.470001,349.089996,350.130005,348.643860,73255500
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2020-10-14,350.750000,351.929993,347.140015,347.929993,346.453186,57958700
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2020-10-15,343.709991,348.019989,343.130005,347.500000,346.025024,60357700
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2020-10-16,348.959991,350.750000,347.100006,347.290009,345.815918,89501900
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2020-10-19,348.649994,349.329987,341.040009,342.010010,340.558319,68425600
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2020-10-20,343.459991,346.880005,342.640015,343.380005,341.922516,60051900
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2020-10-21,343.329987,345.670013,342.399994,342.730011,341.275269,63575000
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2020-10-22,342.959991,345.239990,340.649994,344.609985,343.147278,55399300
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2020-10-23,345.929993,345.989990,343.130005,345.779999,344.312317,49143900
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2020-10-26,342.130005,342.980011,335.619995,339.390015,337.949463,91473000
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2020-10-27,339.760010,340.119995,337.989990,338.220001,336.784393,65994100
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2020-10-28,332.100006,338.250000,326.130005,326.660004,325.273468,127094300
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2020-10-29,326.910004,333.399994,325.089996,329.980011,328.579376,90597700
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2020-10-30,328.279999,329.690002,322.600006,326.540009,325.153992,120287300
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2020-11-02,330.200012,332.359985,327.239990,330.200012,328.798462,86068300
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2020-11-03,333.690002,338.250000,330.290009,336.029999,334.603699,93294200
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2020-11-04,340.859985,347.940002,339.589996,343.540009,342.081818,126959700
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2020-11-05,349.239990,352.190002,348.859985,350.239990,348.753387,82039700
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2020-11-06,349.929993,351.510010,347.649994,350.160004,348.673737,74973000
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2020-11-09,363.970001,364.380005,354.059998,354.559998,353.055054,172304200
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2020-11-10,353.489990,355.179993,350.510010,354.040009,352.537262,85552000
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2020-11-11,356.399994,357.559998,355.059998,356.670013,355.156097,58649000
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2020-11-12,355.579987,356.720001,351.260010,353.209991,351.710754,67546200
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2020-11-13,355.269989,358.899994,354.709991,358.100006,356.580017,62892200
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2020-11-16,360.980011,362.779999,359.589996,362.570007,361.031067,74541100
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2020-11-17,359.970001,361.920013,358.339996,360.619995,359.089325,66111000
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2020-11-18,360.910004,361.500000,356.239990,356.279999,354.767731,70591300
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2020-11-19,355.600006,358.179993,354.149994,357.779999,356.261383,59940900
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2020-11-20,357.500000,357.720001,355.250000,355.329987,353.821777,70417300
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2020-11-23,357.279999,358.820007,354.869995,357.459991,355.942719,63230600
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2020-11-24,360.209991,363.809998,359.290009,363.220001,361.678284,62415900
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2020-11-25,363.130005,363.160004,361.480011,362.660004,361.120667,45330900
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2020-11-27,363.839996,364.179993,362.579987,363.670013,362.126404,28514100
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2020-11-30,362.829987,363.119995,359.170013,362.059998,360.523224,83872700
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2020-12-01,365.570007,367.679993,364.929993,366.019989,364.466400,74505000
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2020-12-02,364.820007,366.959991,364.200012,366.790009,365.233154,45927000
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2020-12-03,366.679993,368.190002,365.500000,366.690002,365.133575,62882000
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2020-12-04,367.320007,369.850006,367.220001,369.850006,368.280151,50749900
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2020-12-07,369.019989,369.619995,367.720001,369.089996,367.523376,48944300
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2020-12-08,367.720001,370.779999,367.670013,370.170013,368.598816,42458900
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2020-12-09,370.880005,371.049988,365.950012,366.850006,365.292877,74098300
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2020-12-10,365.369995,367.859985,364.429993,366.730011,365.173401,57735400
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2020-12-11,364.899994,366.739990,363.260010,366.299988,364.745209,57698600
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2020-12-14,368.640015,369.799988,364.470001,364.660004,363.112183,69216200
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2020-12-15,367.399994,369.589996,365.920013,369.589996,368.021240,64071100
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2020-12-16,369.820007,371.160004,368.869995,370.170013,368.598816,58420500
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2020-12-17,371.940002,372.459991,371.049988,372.239990,370.660004,64119500
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2020-12-18,370.970001,371.149994,367.019989,369.179993,369.179993,136542300
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2020-12-21,364.970001,378.459991,362.029999,367.859985,367.859985,96386700
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2020-12-22,368.209991,368.329987,366.029999,367.239990,367.239990,47949000
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2020-12-23,368.279999,369.619995,367.220001,367.570007,367.570007,46201400
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2020-12-24,368.079987,369.029999,367.450012,369.000000,369.000000,26457900
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2020-12-28,371.739990,372.589996,371.070007,372.170013,372.170013,39000400
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2020-12-29,373.809998,374.000000,370.829987,371.459991,371.459991,53680500
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2020-12-30,372.339996,373.100006,371.570007,371.989990,371.989990,49455300
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2020-12-31,371.779999,374.660004,371.230011,373.880005,373.880005,78469000
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2021-01-04,375.309998,375.450012,364.820007,368.790009,368.790009,110210800
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2021-01-05,368.100006,372.500000,368.049988,371.329987,371.329987,66426200
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2021-01-06,369.709991,376.980011,369.119995,373.549988,373.549988,107997700
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2021-01-07,376.100006,379.899994,375.910004,379.100006,379.100006,68766800
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2021-01-08,380.589996,381.489990,377.100006,381.260010,381.260010,71677200
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2021-01-11,377.850006,380.579987,377.720001,378.690002,378.690002,51176700
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2021-01-12,378.890015,379.859985,376.359985,378.769989,378.769989,52445000

‎examples/hollow_and_filled_candles.ipynb

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‎examples/scratch_pad/hollow_and_filled_images.py

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#!/bin/env python
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import pandas as pd
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import mplfinance as mpf
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Dates = ['2019-11-06', '2019-11-07', '2019-11-08', '2019-11-11', '2019-11-12']
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Open = [3075.1, 3092.0, 3092.0, 3080.33, 3089.28]
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High = [3078.34, 3096.0, 3096.0, 3088.33, 3102.61]
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Low = [3065.89, 3081.0, 3081.0, 3075.82, 3084.73]
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Close = [3076.78, 3085.0, 3085.0, 3087.01, 3091.84]
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Volume = [544288522, 566117910, 460757054, 366044400, 434953689]
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data = dict(Open=Open,High=High,Low=Low,Close=Close,Volume=Volume)
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df = pd.DataFrame(data,index=pd.DatetimeIndex(Dates))
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df.loc['2019-11-07','Open' ] = 3085
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df.loc['2019-11-07','Close'] = 3092
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df.loc['2019-11-07','Low' ] = 3081
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df.loc['2019-11-07','High' ] = 3096
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df.loc['2019-11-08','Open' ] = 3085
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df.loc['2019-11-08','Close'] = 3092
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df.loc['2019-11-08','Low' ] = 3081
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df.loc['2019-11-08','High' ] = 3096
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dfs = df.copy()
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dfs.loc['2019-11-07','Open' ] = 3092
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dfs.loc['2019-11-07','Close'] = 3085
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dfs.loc['2019-11-08','Open' ] = 3092
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dfs.loc['2019-11-08','Close'] = 3085
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df = df.iloc[0:3]
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dfs = dfs.iloc[0:3]
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kwargs=dict(type='hollow_candle',figratio=(8,10),figscale=2.0,update_width_config=dict(candle_linewidth=4.25))
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# s = mpf.make_mpf_style(base_mpf_style='charles',gridstyle='',facecolor='#79c0c3')
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# s = mpf.make_mpf_style(base_mpf_style='charles',gridstyle='',facecolor='gainsboro')
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# s = mpf.make_mpf_style(base_mpf_style='charles',gridstyle='',facecolor='pink')
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# s = mpf.make_mpf_style(base_mpf_style='charles',gridstyle='',facecolor='#ffd6dd')
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s = mpf.make_mpf_style(base_mpf_style='charles',gridstyle='')
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mpf.plot(df ,style=s,**kwargs,savefig='hollow_red_green.jpg')
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mpf.plot(dfs,style=s,**kwargs,savefig='solid_red_green.jpg')
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# st1 = mpf.make_mpf_style(base_mpf_style='checkers',gridstyle='',facecolor='#56b0b3')
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mc = mpf.make_marketcolors(base_mpf_style='checkers',down='#a02128')
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st1 = mpf.make_mpf_style(base_mpf_style='checkers',gridstyle='',facecolor='#79c0c3',marketcolors=mc)
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mpf.plot(df ,style=st1,**kwargs,savefig='hollow_red_blackt.jpg')
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mpf.plot(dfs,style=st1,**kwargs,savefig='solid_red_blackt.jpg')
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st2 = mpf.make_mpf_style(base_mpf_style='checkers',gridstyle='',marketcolors=mc)
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mpf.plot(df ,style=st2,**kwargs,savefig='hollow_red_black.jpg')
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mpf.plot(dfs,style=st2,**kwargs,savefig='solid_red_black.jpg')
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st3 = mpf.make_mpf_style(base_mpf_style='classic',gridstyle='',facecolor='#79c0c3')
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mpf.plot(df ,style=st3,**kwargs,savefig='hollow_black_white.jpg')
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mpf.plot(dfs,style=st3,**kwargs,savefig='solid_black_white.jpg')
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##
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## df = pd.read_csv('data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
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##
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## df.loc['2019-11-07','Open' ] = 3085
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## df.loc['2019-11-07','Close'] = 3092
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## df.loc['2019-11-07','Low' ] = 3081
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## df.loc['2019-11-07','High' ] = 3096
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##
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## df.loc['2019-11-08','Open' ] = 3085
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## df.loc['2019-11-08','Close'] = 3092
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## df.loc['2019-11-08','Low' ] = 3081
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## df.loc['2019-11-08','High' ] = 3096
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##
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##
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## dfs = df.copy()
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## dfs.loc['2019-11-07','Open' ] = 3092
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## dfs.loc['2019-11-07','Close'] = 3085
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##
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## dfs.loc['2019-11-08','Open' ] = 3092
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## dfs.loc['2019-11-08','Close'] = 3085
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##
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## neoclassic = mpf.make_mpf_style(base_mpf_style='classic',facecolor='cyan')
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## st = neoclassic
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## st = mpf.make_mpf_style(base_mpf_style='nightclouds',gridstyle='')
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## #st = mpf.make_mpf_style(base_mpf_style='sas',gridstyle='') #figcolor='#3C8284'
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## st1 = mpf.make_mpf_style(base_mpf_style='classic',gridstyle='',facecolor='#56b0b3')
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## #st1 = mpf.make_mpf_style(base_mpf_style='classic',gridstyle='',facecolor='#ffd6dd')
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## mpf.plot(df.iloc[3:8],type='hollow_candle',volume=False,style=st1,figscale=1,update_width_config=dict(candle_linewidth=2.25))
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## mpf.plot(dfs.iloc[3:8],type='hollow_candle',volume=False,style=st1,figscale=1,update_width_config=dict(candle_linewidth=2.25))
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##

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