Merge branch 'master' of github.com:AurumnPegasus/mplfinance

Author: DanielGoldfarb

.github/workflows/mplfinance_checks.yml

@@ -0,0 +1,76 @@
+name: mplfinance Checks
+on: [ push, pull_request ]
+jobs:
+  Regression_Tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+    steps:
+      - name: Preliminary Information
+        run: |
+          echo "The job was automatically triggered by a ${{ github.event_name }} event."
+          echo "This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+          echo "The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+          echo "  "
+          echo "github.ref = ${{ github.ref }}"
+          echo "github.sha = ${{ github.sha }}"
+          echo "github.event.pull_request.head.ref = ${{ github.event.pull_request.head.ref }}"
+          echo "github.event.pull_request.head.sha = ${{ github.event.pull_request.head.sha }}"
+          echo "github.event.pull_request.base.ref = ${{ github.event.pull_request.base.ref }}"
+          echo "github.event.pull_request.base.sha = ${{ github.event.pull_request.base.sha }}"
+          echo "  "
+
+      - name: Check out repository code
+        uses: actions/checkout@v2
+
+      - run: echo "The ${{ github.repository }} repository has been cloned to the runner."
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pytest
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+      - name: Install My Package
+        run: pip install .
+
+      - name: Run Pytest
+        run: python -m pytest
+
+      - run: echo "This job's status is ${{ job.status }}."
+
+  Pull_Request_Updates_Version:
+    runs-on: ubuntu-latest
+    if: github.event_name == 'pull_request'
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v2
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install packaging
+
+      - name: Fetch base and head on PR
+        if: ${{ github.event.pull_request.base.sha }}
+        run: |
+          git fetch origin master ${{ github.event.pull_request.base.sha }}
+          git fetch origin master ${{ github.event.pull_request.head.sha }}
+
+      - name: Check that Pull Request includes updating the Version
+        run: |
+          git show ${{ github.event.pull_request.base.sha }}:src/mplfinance/_version.py > scripts/tv0.py
+          git show ${{ github.sha }}:src/mplfinance/_version.py > scripts/tv1.py
+          python scripts/version_update_check.py tv0 tv1
+

README.md

@@ -264,7 +264,10 @@ Notice, in the above chart, there are no gaps along the x-coordinate, even thoug
 
 - However, sometimes people like to see these gaps, so that they can tell, with a quick glance, where the weekends and holidays fall.  
 
-- Non-trading days can be displayed with the `show_nontrading` keyword.
+- Non-trading days can be displayed with the **`show_nontrading`** keyword.
+  - Note that for these purposes **non-trading** intervals are those that ***are not represented in the data at all***.  (There are simply no rows for those dates or datetimes).  This is because, when data is retrieved from an exchange or other market data source, that data typically will *not* include rows for non-trading days (weekends and holidays for example).  Thus ...
+  - **`show_nontrading=True`** will display all dates (all time intervals) between the first time stamp and the last time stamp in the data (regardless of whether rows exist for those dates or datetimes).
+  - **`show_nontrading=False`** (the default value) will show ***only*** dates (or datetimes) that have actual rows in the data.  (This means that if there are rows in your DataFrame that exist but contain only **`NaN`** values, these rows *will still appear* on the plot even if **`show_nontrading=False`**)
 - For example, in the chart below, you can easily see weekends, as well as a gap at Thursday, November 28th for the U.S. Thanksgiving holiday.
 
 
@@ -584,7 +587,7 @@ It is my intention to archive the `matplotlib/mpl-finance` repository soon, and
 
 **With this new ` mplfinance ` package installed, in addition to the new API, users can still access the old API**.<br> The old API may be removed someday, but for the foreseeable future we will keep it ... at least until we are very confident that users of the old API can accomplish the same things with the new API.  
 
-To access the old API with the new ` mplfinance ` package installed, change the old import statments
+To access the old API with the new ` mplfinance ` package installed, change the old import statements
 
 **from:**
 

tox.ini renamed to archive.tox.ini

@@ -0,0 +1,21 @@
+import pandas as pd
+import mplfinance as mpf
+
+infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
+
+df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:60]
+
+# mpf.plot(df,figscale=1.5,type='candle',mav=(10,20))
+
+
+
+
+#mpf.plot(df,type='candle',figscale=1.5)
+
+#df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:180]
+#mpf.plot(df,type='renko',figscale=1.5)
+#mpf.plot(df,type='pnf',figscale=1.5)
+
+#mpf.plot(df,type='candle',figscale=1.5,mav=10) 
+
+mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5)

.travis.yml renamed to archive.travis.yml

@@ -0,0 +1,20 @@
+import pandas as pd
+import mplfinance as mpf
+
+infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
+
+df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:60]
+
+import rsi
+df['rsi'] = rsi.relative_strength(df['Close'],n=7)
+
+print(df.head())
+print(df.tail())
+
+apd = mpf.make_addplot(df['rsi'],panel=2,color='lime',ylim=(10,90),secondary_y=True)
+
+mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
+mpf.plot(df,type='candle',style='charles',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
+mpf.plot(df,type='candle',style='mike',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
+mpf.plot(df,type='candle',style='checkers',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
+mpf.plot(df,type='candle',style='yahoo',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))

examples/mpf_demo.py

@@ -0,0 +1,32 @@
+import numpy as np
+def relative_strength(prices, n=14):
+    """
+    compute the n period relative strength indicator
+    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
+    http://www.investopedia.com/terms/r/rsi.asp
+    """
+    deltas = np.diff(prices)
+    seed = deltas[:n + 1]
+    up = seed[seed >= 0].sum() / n
+    down = -seed[seed < 0].sum() / n
+    rs = up / down
+    rsi = np.zeros_like(prices)
+    rsi[:n] = 100. - 100. / (1. + rs)
+
+    for i in range(n, len(prices)):
+        delta = deltas[i - 1]  # cause the diff is 1 shorter
+
+        if delta > 0:
+            upval = delta
+            downval = 0.
+        else:
+            upval = 0.
+            downval = -delta
+
+        up = (up * (n - 1) + upval) / n
+        down = (down * (n - 1) + downval) / n
+
+        rs = up / down
+        rsi[i] = 100. - 100. / (1. + rs)
+
+    return rsi

examples/mpf_rsi_demo.py

@@ -0,0 +1,54 @@
+from select import select
+from datetime import datetime
+
+import time
+import pandas as pd
+import mplfinance as mpf
+import sys
+
+coin = 'BTC' 
+bot_status = 'trading'
+
+def limit():
+    timeout = 2.5
+    print(end='')
+    rlist, _, _ = select([sys.stdin], [], [], timeout)
+    print('rlist=',rlist)
+    if rlist:
+        s = sys.stdin.readline().strip()
+        print('s=',s)
+        if s == 'g':
+            print('\033[1;34m show chart')
+            chart()
+
+
+def dataframe():
+    # response = **_api exchange_**.candles(coin + '-EUR', '1m', {})
+    # ohlcv = []
+    # for s in range(len(response)):
+    #     timestamp = (int(response[s][0]))/1000
+    #     open = float(response[s][1])
+    #     high = float(response[s][2])
+    #     low = float(response[s][3])
+    #     close = float(response[s][4])
+    #     volume = float(response[s][5])
+    #     candles = {'timestamp': (datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')), 'open': open, 'high': high, 'low': low, 'close': close, 'volume': volume}
+    #     ohlcv.append(candles)
+    #     dataframe = pd.DataFrame(data=ohlcv, dtype=float)
+    dataframe = pd.read_csv('../../data/SP500_NOV2019_Hist.csv',index_col=0, parse_dates=True)
+    return dataframe
+
+
+def chart():
+    df = dataframe()
+    df.index = pd.DatetimeIndex(df['timestamp'])
+    df = df.iloc[::-1]
+    s = mpf.make_mpf_style(base_mpf_style='charles', gridcolor='#555555', gridstyle="--", rc={'axes.edgecolor': 'white', 'font.size': 5})
+    fig, axlist = mpf.plot(df, type='candle', style=s, title= coin, ylabel = 'Price (€)', volume=True, warn_too_much_data=9999999, returnfig=True)
+    mpf.show(block=True)
+
+
+# trade loop
+while bot_status == 'trading':
+    limit()
+    print('test')

examples/rsi.py

@@ -0,0 +1,15 @@
+import mplfinance as mpf
+import pandas as pd
+
+#daily = pd.read_csv('/Users/jsb/Desktop/mplfinance-master/examples/data/SP500_NOV2019_Hist.csv',
+#                    index_col=0, parse_dates=True)
+daily = pd.read_csv('../../data/SP500_NOV2019_Hist.csv',
+                    index_col=0, parse_dates=True)
+daily.index.name = 'Date'
+# print(daily.shape)
+print(daily)
+# daily = daily.loc[:, ['Open', 'High', 'Low', 'Close', 'Volume']]
+
+mpf.figure(figsize=(20, 8), dpi=100)
+
+mpf.plot(daily, type='candle', tight_layout=True)

examples/scratch_pad/issues/issue436.py

@@ -0,0 +1,108 @@
+###   !pip install yfinance
+###   !pip install mplfinance
+import yfinance as yf
+import mplfinance as mpf
+import numpy as np 
+import pandas as pd 
+
+# get the data from yfiance 
+df=yf.download('BTC-USD',start='2008-01-04',end='2021-06-3',interval='1d')
+
+#code snippet 5.1
+# Fit linear regression on close
+# Return the t-statistic for a given parameter estimate.
+def tValLinR(close):
+    #tValue from a linear trend
+    x = np.ones((close.shape[0],2))
+    x[:,1] = np.arange(close.shape[0])
+    ols = sm1.OLS(close, x).fit()
+    return ols.tvalues[1]
+
+    #code snippet 5.2
+'''
+ #search for the maximum absolutet-value. To identify the trend 
+  #  - molecule - index of observations we wish to labels. 
+   # - close - which is the time series of x_t
+   # - span - is the set of values of L (look forward period) that the algorithm will #try (window_size)
+#    The L that maximizes |tHat_B_1| (t-value) is choosen - which is the look-forward #period 
+#    with the most significant trend. (optimization)
+'''
+def getBinsFromTrend(molecule, close, span):
+    
+    #Derive labels from the sign of t-value of trend line
+    #output includes:
+     # - t1: End time for the identified trend
+     # - tVal: t-value associated with the estimated trend coefficient
+      #- bin: Sign of the trend (1,0,-1)
+    #The t-statistics for each tick has a different look-back window.
+      
+    #- idx start time in look-forward window
+    #- dt1 stop time in look-forward window
+    #- df1 is the look-forward window (window-size)
+    #- iloc ? 
+    
+    out = pd.DataFrame(index=molecule, columns=['t1', 'tVal', 'bin', 'windowSize'])
+    hrzns = range(*span)
+    windowSize = span[1] - span[0]
+    maxWindow = span[1]-1
+    minWindow = span[0]
+    for idx in close.index:
+        idx += (maxWindow*pd.Timedelta('1 day'))
+        if idx >= close.index[-1]:
+            break
+        df_tval = pd.Series(dtype='float64')
+        iloc0 = close.index.get_loc(idx)
+        if iloc0+max(hrzns) > close.shape[0]:
+            continue
+        for hrzn in hrzns:
+            dt1 = close.index[iloc0-hrzn+1]
+            df1 = close.loc[dt1:idx]
+            df_tval.loc[dt1] = tValLinR(df1.values) #calculates t-statistics on period
+        dt1 = df_tval.replace([-np.inf, np.inf, np.nan], 0).abs().idxmax() #get largest t-statistics calculated over span period
+
+        print(df_tval.index[-1])
+        print(dt1)
+        print(abs(df_tval.values).argmax() + minWindow)
+        out.loc[idx, ['t1', 'tVal', 'bin', 'windowSize']] = df_tval.index[-1], df_tval[dt1], np.sign(df_tval[dt1]), abs(df_tval.values).argmax() + minWindow #prevent leakage
+    out['t1'] = pd.to_datetime(out['t1'])
+    out['bin'] = pd.to_numeric(out['bin'], downcast='signed')
+
+    #deal with massive t-Value outliers - they dont provide more confidence and they ruin the scatter plot
+    tValueVariance = out['tVal'].values.var()
+    tMax = 20
+    if tValueVariance < tMax:
+        tMax = tValueVariance
+
+    out.loc[out['tVal'] > tMax, 'tVal'] = tMax #cutoff tValues > 20
+    out.loc[out['tVal'] < (-1)*tMax, 'tVal'] = (-1)*tMax #cutoff tValues < -20
+    return out.dropna(subset=['bin'])
+
+if __name__ == '__main__':
+    #snippet 5.3
+    idx_range_from = 3
+    idx_range_to = 10
+    df1 = getBinsFromTrend(df.index, df['Close'], [idx_range_from,idx_range_to,1]) #[3,10,1] = range(3,10) This is the issue 
+    tValues = df1['tVal'].values #tVal
+
+    doNormalize = False
+    #normalise t-values to -1, 1
+    if doNormalize:
+        np.min(tValues)
+        minusArgs = [i for i in range(0, len(tValues)) if tValues[i] < 0]
+        tValues[minusArgs] = tValues[minusArgs] / (np.min(tValues)*(-1.0))
+
+        plus_one = [i for i in range(0, len(tValues)) if tValues[i] > 0]
+        tValues[plus_one] = tValues[plus_one] / np.max(tValues)
+
+    #+(idx_range_to-idx_range_from+1)
+    plt.scatter(df1.index, df0.loc[df1.index].values, c=tValues, cmap='viridis') #df1['tVal'].values, cmap='viridis')
+    plt.plot(df0.index, df0.values, color='gray')
+    plt.colorbar()
+    plt.show()
+    plt.savefig('fig5.2.png')
+    plt.clf()
+    plt.df['Close']()
+    plt.scatter(df1.index, df0.loc[df1.index].values, c=df1['bin'].values,  cmap='vipridis')
+
+    #Test methods
+    ols_tvalue = tValLinR( np.array([3.0, 3.5, 4.0]) )

examples/scratch_pad/issues/issue438.py

@@ -0,0 +1,2 @@
+[pytest]
+python_files = tests/*

examples/scratch_pad/issues/so68871906.py

@@ -0,0 +1,37 @@
+import os
+import sys
+import importlib
+from   packaging import version
+
+if len(sys.argv) < 3:
+   raise RuntimeError('Got less than 2 Version Arguments!')
+
+debug = True if (len(sys.argv) > 3 and sys.argv[3] == 'debug') else False
+
+v0  = importlib.import_module(sys.argv[1])
+pv0 = version.parse(v0.__version__)
+
+v1  = importlib.import_module(sys.argv[2])
+pv1 = version.parse(v1.__version__)
+
+if debug:
+    print('sys.argv=',sys.argv)
+    print('v0=',v0)
+    print('v1=',v1)
+    print('pv0=',pv0)
+    print('pv1=',pv1)
+#   cmd='cat '+sys.argv[1]+'.py'
+#   print('os.system("'+cmd+'")')
+#   os.system(cmd)
+#   cmd='cat '+sys.argv[2]+'.py'
+#   print('os.system("'+cmd+'")')
+#   os.system(cmd)
+    print('v0.__version__=',v0.__version__)
+    print('v1.__version__=',v1.__version__)
+
+if not pv1 > pv0:
+    print('ERROR: Pull Request requires mplfinance version to be updated: (Version '+str(pv1)+' is NOT greater than '+str(pv0)+')')
+    exit(1)
+else:
+    print('Version was updated OK (from '+str(pv0)+' to '+str(pv1)+')')
+    exit(0)