add some issues

Author: DanielGoldfarb

examples/scratch_pad/issues/issue436.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/scratch_pad/issues/issue438.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/so68871906.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]) )