All information provided here is the personal opinion of the authors. The authors, AAII, AAII-Silicon Valley, AAII-SV-CI Group leaders and members, make no claim that the information in any of these presentations is correct. Under no circumstances should any of the information in these presentations be taken as personal or individual trading advice. Past performance and/or hypothetical results of any trading method are not indicative of future results. Trading and investing in any financial instrument carries high levels of risk and may not be suitable for all investors. You should be aware of all the risks associated with trading, and seek advice from a financial professional, if you have any doubts.
The authors, AAII, AAII-Silicon Valley, AAII-SV-CI Group leaders and members, will not assume any responsibility whatsoever for the actions of the reader nor financial losses that may result from the use or misuse of the information presented.
import pandas_datareader as pdr
from datetime import datetime, timedelta
def sign(b):
return (0 < b) - (0 > b)
print(sign(-3.2))
-1
print(sign(3.2))
1
print(sign(0.0))
0
df= pdr.get_data_yahoo("%5EGSPC", datetime(1936, 1, 1), datetime.now())["Adj Close"].to_frame()
print(df)
Adj Close Date 1936-01-02 13.400000 1936-01-03 13.550000 1936-01-06 13.410000 1936-01-07 13.620000 1936-01-08 13.700000 ... ... 2022-10-28 3901.060059 2022-10-31 3871.979980 2022-11-01 3856.100098 2022-11-02 3759.689941 2022-11-03 3719.889893 [21832 rows x 1 columns]
df["pct_change"]= df['Adj Close'].pct_change()
print(df)
Adj Close pct_change Date 1936-01-02 13.400000 NaN 1936-01-03 13.550000 0.011194 1936-01-06 13.410000 -0.010332 1936-01-07 13.620000 0.015660 1936-01-08 13.700000 0.005874 ... ... ... 2022-10-28 3901.060059 0.024626 2022-10-31 3871.979980 -0.007454 2022-11-01 3856.100098 -0.004101 2022-11-02 3759.689941 -0.025002 2022-11-03 3719.889893 -0.010586 [21832 rows x 2 columns]
df["sign"]= df['pct_change'].map(sign) #also works with apply(sign)
print(df)
Adj Close pct_change sign Date 1936-01-02 13.400000 NaN 0 1936-01-03 13.550000 0.011194 1 1936-01-06 13.410000 -0.010332 -1 1936-01-07 13.620000 0.015660 1 1936-01-08 13.700000 0.005874 1 ... ... ... ... 2022-10-25 3859.110107 0.016267 1 2022-10-26 3830.600098 -0.007388 -1 2022-10-27 3807.300049 -0.006083 -1 2022-10-28 3901.060059 0.024626 1 2022-10-31 3871.979980 -0.007454 -1 [21829 rows x 3 columns]
print(df.loc[df['sign']==0])
Adj Close pct_change sign Date 1936-01-02 13.400000 NaN 0 1936-05-12 13.860000 0.0 0 1936-05-15 14.290000 0.0 0 1936-06-02 14.390000 0.0 0 1936-12-15 17.320000 0.0 0 ... ... ... ... 1992-09-03 417.980011 0.0 0 1997-01-28 765.020020 0.0 0 2003-01-10 927.570007 0.0 0 2008-01-03 1447.160034 0.0 0 2017-01-10 2268.899902 0.0 0 [275 rows x 3 columns]
df["ffill"]= df['sign'].replace(to_replace=0, method='ffill')
print(df.loc[df['sign']==0])
Adj Close pct_change sign ffill Date 1936-01-02 13.400000 NaN 0 0 1936-05-12 13.860000 0.0 0 -1 1936-05-15 14.290000 0.0 0 1 1936-06-02 14.390000 0.0 0 -1 1936-12-15 17.320000 0.0 0 1 ... ... ... ... ... 1992-09-03 417.980011 0.0 0 1 1997-01-28 765.020020 0.0 0 -1 2003-01-10 927.570007 0.0 0 1 2008-01-03 1447.160034 0.0 0 -1 2017-01-10 2268.899902 0.0 0 -1 [275 rows x 4 columns]
window=70
df["rollingSum"]= df['ffill'].rolling(window).sum()
print(df)
Adj Close pct_change sign ffill rollingSum Date 1936-01-02 13.400000 NaN 0 0 NaN 1936-01-03 13.550000 0.011194 1 1 NaN 1936-01-06 13.410000 -0.010332 -1 -1 NaN 1936-01-07 13.620000 0.015660 1 1 NaN 1936-01-08 13.700000 0.005874 1 1 NaN ... ... ... ... ... ... 2022-10-25 3859.110107 0.016267 1 1 -8.0 2022-10-26 3830.600098 -0.007388 -1 -1 -10.0 2022-10-27 3807.300049 -0.006083 -1 -1 -12.0 2022-10-28 3901.060059 0.024626 1 1 -12.0 2022-10-31 3871.979980 -0.007454 -1 -1 -12.0 [21829 rows x 5 columns]
downLimit=-17
df["downLimit"]= df['rollingSum']<=downLimit
print(df)
Adj Close pct_change sign ffill rollingSum downLimit Date 1936-01-02 13.400000 NaN 0 0 NaN False 1936-01-03 13.550000 0.011194 1 1 NaN False 1936-01-06 13.410000 -0.010332 -1 -1 NaN False 1936-01-07 13.620000 0.015660 1 1 NaN False 1936-01-08 13.700000 0.005874 1 1 NaN False ... ... ... ... ... ... ... 2022-10-25 3859.110107 0.016267 1 1 -8.0 False 2022-10-26 3830.600098 -0.007388 -1 -1 -10.0 False 2022-10-27 3807.300049 -0.006083 -1 -1 -12.0 False 2022-10-28 3901.060059 0.024626 1 1 -12.0 False 2022-10-31 3871.979980 -0.007454 -1 -1 -12.0 False [21829 rows x 6 columns]
df["weekday"]=[df.index[i].weekday() for i in range(len(df))] #0 is Monday, 4 is Friday
print(df)
Adj Close Prolonged Liquidation weekday Date 1936-01-02 13.400000 False 3 1936-01-03 13.550000 False 4 1936-01-06 13.410000 False 0 1936-01-07 13.620000 False 1 1936-01-08 13.700000 False 2 ... ... ... ... 2022-10-27 3807.300049 False 3 2022-10-28 3901.060059 False 4 2022-10-31 3871.979980 False 0 2022-11-01 3856.100098 False 1 2022-11-02 3832.639893 False 2 [21831 rows x 3 columns]
aDf= df.loc[df["downLimit"] & (4==df["weekday"])] # 4 is Friday
print(aDf.to_string()) #Note: 'to_string' eliminates elipses in print
Adj Close pct_change sign ffill rollingSum downLimit weekday Date 1937-11-05 11.460000 0.000000 0 -1 -18.0 True 4 1937-11-12 11.910000 -0.004181 -1 -1 -18.0 True 4 1937-11-19 10.510000 -0.058244 -1 -1 -22.0 True 4 1937-11-26 10.670000 0.049164 1 1 -22.0 True 4 1938-04-01 8.910000 0.048235 1 1 -18.0 True 4 1941-12-12 8.730000 -0.010204 -1 -1 -20.0 True 4 1941-12-19 8.490000 0.000000 0 -1 -24.0 True 4 1941-12-26 8.420000 0.003576 1 1 -24.0 True 4 1942-01-02 8.890000 0.023015 1 1 -26.0 True 4 1942-01-09 8.850000 -0.003378 -1 -1 -26.0 True 4 1942-01-16 8.960000 -0.008850 -1 -1 -24.0 True 4 1942-01-23 8.840000 0.003405 1 1 -22.0 True 4 1942-01-30 8.850000 -0.006734 -1 -1 -20.0 True 4 1942-02-06 8.850000 -0.011173 -1 -1 -24.0 True 4 1942-02-13 8.620000 0.002326 1 1 -22.0 True 4 1942-02-20 8.470000 -0.003529 -1 -1 -22.0 True 4 1942-02-27 8.590000 0.008216 1 1 -18.0 True 4 1942-03-06 8.160000 -0.026253 -1 -1 -22.0 True 4 1942-03-13 8.030000 0.006266 1 1 -18.0 True 4 1942-03-20 8.110000 -0.010976 -1 -1 -20.0 True 4 1942-03-27 8.050000 -0.009840 -1 -1 -18.0 True 4 1942-04-17 7.700000 -0.015345 -1 -1 -18.0 True 4 1956-10-26 46.270000 0.009160 1 1 -18.0 True 4 1956-11-02 46.980000 0.009888 1 1 -18.0 True 4 1956-11-09 46.340000 -0.008346 -1 -1 -20.0 True 4 1956-11-16 45.740002 0.000437 1 1 -24.0 True 4 1956-11-23 45.139999 0.010522 1 1 -22.0 True 4 1956-11-30 45.080002 0.015773 1 1 -20.0 True 4 1957-11-01 40.439999 -0.015100 -1 -1 -18.0 True 4 1962-05-11 62.650002 -0.014472 -1 -1 -18.0 True 4 1962-05-18 63.820000 -0.001721 -1 -1 -24.0 True 4 1962-05-25 59.470001 -0.018971 -1 -1 -26.0 True 4 1962-06-01 59.380001 -0.004193 -1 -1 -26.0 True 4 1962-06-08 58.450001 0.000856 1 1 -22.0 True 4 1962-06-15 55.889999 0.028713 1 1 -22.0 True 4 1962-06-22 52.680000 -0.016981 -1 -1 -30.0 True 4 1962-06-29 54.750000 0.006249 1 1 -26.0 True 4 1962-07-06 56.169998 -0.011266 -1 -1 -22.0 True 4 1970-04-17 85.669998 -0.002445 -1 -1 -18.0 True 4 1970-04-24 82.769997 -0.003251 -1 -1 -20.0 True 4 1970-05-01 81.440002 -0.000981 -1 -1 -24.0 True 4 1970-05-08 79.440002 -0.004885 -1 -1 -22.0 True 4 1970-05-15 76.900002 0.019353 1 1 -24.0 True 4 1970-05-22 72.250000 0.001247 1 1 -26.0 True 4 1970-05-29 76.550003 0.026002 1 1 -22.0 True 4 1970-06-05 76.169998 -0.015383 -1 -1 -22.0 True 4 1970-06-12 73.879997 -0.007656 -1 -1 -26.0 True 4 1970-06-19 77.050003 0.007058 1 1 -20.0 True 4 1970-06-26 73.470001 -0.007430 -1 -1 -22.0 True 4 1970-07-10 74.449997 0.005266 1 1 -22.0 True 4 1974-02-01 95.320000 -0.012944 -1 -1 -18.0 True 4 1974-02-08 92.330002 -0.010397 -1 -1 -20.0 True 4 1974-08-23 71.550003 -0.017170 -1 -1 -18.0 True 4 1974-09-13 65.199997 -0.022635 -1 -1 -24.0 True 4 1974-09-20 70.139999 0.000713 1 1 -20.0 True 4 1974-09-27 64.940002 -0.022871 -1 -1 -22.0 True 4 1974-10-04 62.340000 0.000963 1 1 -24.0 True 4 1974-10-11 71.139999 0.019344 1 1 -18.0 True 4 1974-10-25 70.120003 -0.001424 -1 -1 -20.0 True 4 1974-11-01 73.879997 -0.000271 -1 -1 -22.0 True 4 1974-11-08 74.910004 -0.003989 -1 -1 -18.0 True 4 1974-11-15 71.910004 -0.015740 -1 -1 -22.0 True 4 1974-11-22 68.900002 0.010560 1 1 -18.0 True 4 1982-07-30 107.089996 -0.005849 -1 -1 -18.0 True 4 1982-08-06 103.709999 -0.013789 -1 -1 -22.0 True 4 1982-08-13 103.849998 0.013962 1 1 -28.0 True 4 1982-08-20 113.019997 0.035361 1 1 -26.0 True 4 1982-08-27 117.110001 -0.012147 -1 -1 -20.0 True 4 1984-03-09 154.350006 -0.005413 -1 -1 -18.0 True 4 1984-04-13 157.309998 -0.002663 -1 -1 -18.0 True 4 2000-12-15 1312.150024 -0.021463 -1 -1 -18.0 True 4 2001-09-21 965.799988 -0.019034 -1 -1 -18.0 True 4 2002-07-19 847.750000 -0.038352 -1 -1 -20.0 True 4 2002-07-26 852.840027 0.016884 1 1 -20.0 True 4
Calculate sign of Adj Close's percent change. Zero fill. Mark values <=17 in 70 day rolling window. Note: 70 days may not be the same as 14 weeks!
import pandas_datareader as pdr
from datetime import datetime, timedelta
sign = lambda b: (0 < b) - (0 > b) # Lambda is python's shorthand for creating one line functions
df= pdr.get_data_yahoo("%5EGSPC", datetime(1936, 1, 1), datetime.now())["Adj Close"].to_frame()
window=70
downLimit=-17
df["Prolonged Liquidation"]= df['Adj Close'].pct_change().map(sign).replace(to_replace=0, method='ffill').rolling(window).sum()<=downLimit
df["weekday"]=[df.index[i].weekday() for i in range(len(df))]
aDf= df.loc[df["Prolonged Liquidation"] & (4==df["weekday"])] # 4 is Friday
print(aDf.to_string())
Adj Close Prolonged Liquidation weekday Date 1937-11-05 11.460000 True 4 1937-11-12 11.910000 True 4 1937-11-19 10.510000 True 4 1937-11-26 10.670000 True 4 1938-04-01 8.910000 True 4 1941-12-12 8.730000 True 4 1941-12-19 8.490000 True 4 1941-12-26 8.420000 True 4 1942-01-02 8.890000 True 4 1942-01-09 8.850000 True 4 1942-01-16 8.960000 True 4 1942-01-23 8.840000 True 4 1942-01-30 8.850000 True 4 1942-02-06 8.850000 True 4 1942-02-13 8.620000 True 4 1942-02-20 8.470000 True 4 1942-02-27 8.590000 True 4 1942-03-06 8.160000 True 4 1942-03-13 8.030000 True 4 1942-03-20 8.110000 True 4 1942-03-27 8.050000 True 4 1942-04-17 7.700000 True 4 1956-10-26 46.270000 True 4 1956-11-02 46.980000 True 4 1956-11-09 46.340000 True 4 1956-11-16 45.740002 True 4 1956-11-23 45.139999 True 4 1956-11-30 45.080002 True 4 1957-11-01 40.439999 True 4 1962-05-11 62.650002 True 4 1962-05-18 63.820000 True 4 1962-05-25 59.470001 True 4 1962-06-01 59.380001 True 4 1962-06-08 58.450001 True 4 1962-06-15 55.889999 True 4 1962-06-22 52.680000 True 4 1962-06-29 54.750000 True 4 1962-07-06 56.169998 True 4 1970-04-17 85.669998 True 4 1970-04-24 82.769997 True 4 1970-05-01 81.440002 True 4 1970-05-08 79.440002 True 4 1970-05-15 76.900002 True 4 1970-05-22 72.250000 True 4 1970-05-29 76.550003 True 4 1970-06-05 76.169998 True 4 1970-06-12 73.879997 True 4 1970-06-19 77.050003 True 4 1970-06-26 73.470001 True 4 1970-07-10 74.449997 True 4 1974-02-01 95.320000 True 4 1974-02-08 92.330002 True 4 1974-08-23 71.550003 True 4 1974-09-13 65.199997 True 4 1974-09-20 70.139999 True 4 1974-09-27 64.940002 True 4 1974-10-04 62.340000 True 4 1974-10-11 71.139999 True 4 1974-10-25 70.120003 True 4 1974-11-01 73.879997 True 4 1974-11-08 74.910004 True 4 1974-11-15 71.910004 True 4 1974-11-22 68.900002 True 4 1982-07-30 107.089996 True 4 1982-08-06 103.709999 True 4 1982-08-13 103.849998 True 4 1982-08-20 113.019997 True 4 1982-08-27 117.110001 True 4 1984-03-09 154.350006 True 4 1984-04-13 157.309998 True 4 2000-12-15 1312.150024 True 4 2001-09-21 965.799988 True 4 2002-07-19 847.750000 True 4 2002-07-26 852.840027 True 4
Compute the return for the next month, quarter, 6 months and year return AFTER each of bottom signals is made. I haven’t seen any metrics which show Alphier’s bottom picker had any value – only when it fired. And it doesn’t seem to be doing too well OOS for ^GSPC since last signal was in 2002. It totally missed 2008-2009.
import pandas_datareader as pdr
import pandas as pd
from datetime import datetime, timedelta
sign = lambda b: (0 < b) - (0 > b)
IdxTkrs=[
["DJI" , "%5EDJI"],
["N100" , "%5ENDX"],
["SP500" , "%5EGSPC"],
["SP400" , "%5ESP400"],
["SP600" , "%5ESP600"],
["R1000" , "%5ERUI"],
["R2000" , "%5ERUT"],
]
def Alphier(df,window=70,downLimit=-17):
df["ProLiq"]= df['Adj Close'].pct_change().map(sign).replace(to_replace=0, method='ffill').rolling(window).sum()<=downLimit
df["weekday"]=[df.index[i].weekday() for i in range(len(df))]
aDf= df.loc[df["ProLiq"] & (4==df["weekday"])] # 4 is Friday
return aDf[['Adj Close']]
print(aDf.to_string())
def IndexComparison(IdxTkrs,window=70,downLimit=-17):
deltas=[30,int(365/4),int(365/2),365]
rows=[]
for Idx, Tkr in IdxTkrs:
print("\n",Idx," returns over 1,3,6 and 12 months")
iDf= pdr.get_data_yahoo(Tkr, datetime(1936, 1, 1), datetime.now())["Adj Close"].to_frame()
aDf=Alphier(iDf,window,downLimit)
# df hold the 1,3,6 and 12 mo quotes after each bottom in aDf
df=pd.DataFrame([[iDf.loc[idx+timedelta(delta):].iloc[0]["Adj Close"] for delta in deltas]
for idx in aDf.index],columns=deltas,index=aDf.index)
df=pd.concat([aDf,df],axis=1)
# Turn Quotes into changes
for delta in deltas: df['%d chg'%delta] = (df[delta]-aDf["Adj Close"])/df["Adj Close"]
# rows holds one summary statistic for each Idx
rows.append([Idx]+[df['%d chg'%delta].mean() for delta in deltas])
print(df.to_string())
return pd.DataFrame(rows,columns=["Index"]+deltas)
IndexComparisonDf=IndexComparison(IdxTkrs)
DJI returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
2001-09-21 8235.809570 9377.030273 10035.339844 10427.669922 7872.149902 0.138568 0.218501 0.266138 -0.044156
2002-07-19 8019.259766 8990.790039 8322.400391 8586.740234 9096.690430 0.121150 0.037802 0.070765 0.134355
2002-07-26 8264.389648 8919.009766 8443.990234 8131.009766 9266.509766 0.079210 0.021732 -0.016139 0.121258
2002-08-30 8663.500000 7591.930176 8896.089844 7891.080078 9523.269531 -0.123688 0.026847 -0.089158 0.099240
N100 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
1992-04-24 303.815002 305.894989 303.424988 325.440002 327.279999 0.006846 -0.001284 0.071178 0.077234
1992-05-01 307.475006 318.980011 310.894989 329.154999 344.970001 0.037418 0.011123 0.070510 0.121945
1992-05-15 303.015015 303.864990 302.230011 348.489990 352.670013 0.002805 -0.002591 0.150075 0.163870
2000-12-01 2549.739990 2128.780029 1881.339966 1840.829956 1567.540039 -0.165099 -0.262144 -0.278032 -0.385216
2000-12-08 2895.389893 2281.540039 1813.020020 1896.219971 1645.359985 -0.212009 -0.373825 -0.345090 -0.431731
2000-12-15 2543.090088 2470.719971 1647.510010 1701.530029 1640.339966 -0.028458 -0.352162 -0.330920 -0.354982
2000-12-22 2436.260010 2643.129883 1705.020020 1727.469971 1577.310059 0.084913 -0.300149 -0.290934 -0.352569
2000-12-29 2341.699951 2694.530029 1573.250000 1830.189941 1577.050049 0.150673 -0.328159 -0.218435 -0.326536
2001-01-05 2267.850098 2467.300049 1448.160034 1668.589966 1649.829956 0.087947 -0.361439 -0.264242 -0.272514
2002-05-10 1188.780029 1135.619995 937.330017 1008.349976 1160.420044 -0.044718 -0.211519 -0.151777 -0.023856
2006-07-14 1462.170044 1494.329956 1727.209961 1844.810059 2028.020020 0.021995 0.181265 0.261693 0.386993
2006-07-28 1510.300049 1570.290039 1717.609985 1772.969971 1973.930054 0.039721 0.137264 0.173919 0.306979
2008-11-21 1085.569946 1189.150024 1172.709961 1363.170044 1792.939941 0.095415 0.080271 0.255718 0.651612
SP500 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
1937-11-05 11.460000 11.130000 10.540000 10.430000 13.430000 -0.028796 -0.080279 -0.089878 0.171902
1937-11-12 11.910000 10.740000 10.930000 10.260000 13.430000 -0.098237 -0.082284 -0.138539 0.127624
1937-11-19 10.510000 11.310000 11.020000 9.960000 12.880000 0.076118 0.048525 -0.052331 0.225500
1937-11-26 10.670000 10.690000 11.570000 9.280000 12.370000 0.001874 0.084349 -0.130272 0.159325
1938-04-01 8.910000 9.590000 11.910000 12.240000 11.260000 0.076319 0.336700 0.373737 0.263749
1941-12-12 8.730000 8.840000 8.030000 8.320000 9.420000 0.012600 -0.080183 -0.046964 0.079038
1941-12-19 8.490000 8.960000 8.110000 8.400000 9.680000 0.055359 -0.044759 -0.010601 0.140165
1941-12-26 8.420000 8.970000 8.050000 8.230000 9.650000 0.065321 -0.043943 -0.022565 0.146081
1942-01-02 8.890000 8.840000 8.200000 8.440000 9.920000 -0.005624 -0.077615 -0.050619 0.115860
1942-01-09 8.850000 8.740000 7.980000 8.800000 9.960000 -0.012429 -0.098305 -0.005650 0.125424
1942-01-16 8.960000 8.650000 7.700000 8.720000 10.120000 -0.034598 -0.140625 -0.026786 0.129464
1942-01-23 8.840000 8.520000 7.550000 8.630000 10.300000 -0.036199 -0.145928 -0.023756 0.165158
1942-01-30 8.850000 8.470000 7.710000 8.560000 10.520000 -0.042938 -0.128814 -0.032768 0.188701
1942-02-06 8.850000 8.200000 7.890000 8.490000 10.500000 -0.073446 -0.108475 -0.040678 0.186441
1942-02-13 8.620000 8.120000 7.880000 8.580000 10.810000 -0.058005 -0.085847 -0.004640 0.254060
1942-02-20 8.470000 8.150000 8.010000 8.710000 10.840000 -0.037780 -0.054309 0.028335 0.279811
1942-02-27 8.590000 8.060000 8.150000 8.610000 10.920000 -0.061700 -0.051222 0.002328 0.271246
1942-03-06 8.160000 8.200000 8.370000 8.610000 11.010000 0.004902 0.025735 0.055147 0.349265
1942-03-13 8.030000 7.960000 8.320000 8.540000 11.040000 -0.008717 0.036115 0.063512 0.374844
1942-03-20 8.110000 7.790000 8.400000 8.660000 10.920000 -0.039457 0.035758 0.067818 0.346486
1942-03-27 8.050000 7.540000 8.230000 8.900000 11.490000 -0.063354 0.022360 0.105590 0.427329
1942-04-17 7.700000 7.940000 8.720000 9.350000 11.380000 0.031169 0.132468 0.214286 0.477922
1956-10-26 46.270000 44.869999 44.820000 45.500000 40.419998 -0.030257 -0.031338 -0.016641 -0.126432
1956-11-02 46.980000 45.980000 44.619999 46.340000 40.369999 -0.021286 -0.050234 -0.013623 -0.140698
1956-11-09 46.340000 46.799999 43.320000 46.590000 40.180000 0.009927 -0.065170 0.005395 -0.132931
1956-11-16 45.740002 46.540001 43.509998 47.150002 40.040001 0.017490 -0.048754 0.030826 -0.124617
1956-11-23 45.139999 46.389999 43.380001 47.209999 41.180000 0.027692 -0.038990 0.045857 -0.087727
1956-11-30 45.080002 46.669998 43.740002 47.430000 41.360001 0.035271 -0.029725 0.052130 -0.082520
1957-11-01 40.439999 41.360001 41.700001 43.689999 51.560001 0.022750 0.031157 0.080366 0.274975
1962-05-11 62.650002 57.820000 57.549999 58.779999 70.480003 -0.077095 -0.081405 -0.061772 0.124980
1962-05-18 63.820000 55.740002 59.009998 60.160000 69.959999 -0.126606 -0.075368 -0.057349 0.096208
1962-05-25 59.470001 52.450001 59.580002 61.540001 69.870003 -0.118043 0.001850 0.034807 0.174878
1962-06-01 59.380001 55.860001 59.119999 62.259998 70.690002 -0.059279 -0.004379 0.048501 0.190468
1962-06-08 58.450001 56.549999 58.380001 63.060001 69.940002 -0.032506 -0.001198 0.078871 0.196578
1962-06-15 55.889999 57.830002 58.889999 62.570000 69.949997 0.034711 0.053677 0.119520 0.251566
1962-06-22 52.680000 56.799999 57.689999 62.639999 70.199997 0.078208 0.095102 0.189066 0.332574
1962-06-29 54.750000 57.830002 56.270000 62.959999 68.860001 0.056256 0.027763 0.149954 0.257717
1962-07-06 56.169998 57.750000 57.070000 64.129997 69.739998 0.028129 0.016023 0.141713 0.241588
1970-04-17 85.669998 76.959999 77.690002 84.279999 104.010002 -0.101669 -0.093148 -0.016225 0.214077
1970-04-24 82.769997 70.250000 77.820000 83.769997 103.940002 -0.151263 -0.059804 0.012082 0.255769
1970-05-01 81.440002 77.839996 78.050003 83.250000 103.290001 -0.044204 -0.041626 0.022225 0.268296
1970-05-08 79.440002 76.290001 77.279999 84.220001 102.360001 -0.039653 -0.027190 0.060171 0.288520
1970-05-15 76.900002 74.580002 75.180000 83.370003 100.690002 -0.030169 -0.022367 0.084135 0.309363
1970-05-22 72.250000 76.639999 79.239998 83.720001 100.129997 0.060761 0.096747 0.158754 0.385882
1970-05-29 76.550003 72.889999 81.860001 85.930000 100.199997 -0.047812 0.069366 0.122534 0.308948
1970-06-05 76.169998 71.779999 82.830002 89.459999 101.089996 -0.057634 0.087436 0.174478 0.327163
1970-06-12 73.879997 74.550003 82.519997 90.260002 100.220001 0.009069 0.116946 0.221711 0.356524
1970-06-19 77.050003 77.790001 82.620003 90.220001 97.870003 0.009604 0.072291 0.170928 0.270214
1970-06-26 73.470001 77.650002 82.830002 91.089996 97.739998 0.056894 0.127399 0.239826 0.330339
1970-07-10 74.449997 76.199997 85.080002 92.190002 100.820000 0.023506 0.142780 0.238281 0.354197
1974-02-01 95.320000 95.529999 91.290001 78.589996 77.820000 0.002203 -0.042279 -0.175514 -0.183592
1974-02-08 92.330002 98.879997 91.470001 80.860001 78.360001 0.070941 -0.009314 -0.124228 -0.151305
1974-08-23 71.550003 69.419998 68.900002 82.620003 85.059998 -0.029769 -0.037037 0.154717 0.188819
1974-09-13 65.199997 72.739998 67.070000 84.760002 82.879997 0.115644 0.028681 0.300000 0.271166
1974-09-20 70.139999 73.500000 66.910004 83.389999 85.070000 0.047904 -0.046051 0.188908 0.212860
1974-09-27 64.940002 70.089996 67.139999 83.360001 85.029999 0.079304 0.033877 0.283646 0.309362
1974-10-04 62.340000 73.080002 70.709999 80.879997 86.879997 0.172281 0.134264 0.297401 0.393648
1974-10-11 71.139999 75.150002 72.610001 84.180000 89.459999 0.056368 0.020663 0.183301 0.257520
1974-10-25 70.120003 68.830002 72.980003 86.620003 89.730003 -0.018397 0.040787 0.235311 0.279663
1974-11-01 73.879997 68.110001 76.980003 89.220001 88.089996 -0.078100 0.041960 0.207634 0.192339
1974-11-08 74.910004 65.599998 78.629997 90.529999 89.339996 -0.124283 0.049660 0.208517 0.192631
1974-11-15 71.910004 66.459999 81.500000 90.430000 91.459999 -0.075789 0.133361 0.257544 0.271868
1974-11-22 68.900002 65.959999 82.620003 90.580002 89.699997 -0.042671 0.199129 0.314659 0.301887
1982-07-30 107.089996 117.660004 133.720001 144.509995 162.039993 0.098702 0.248669 0.349426 0.513120
1982-08-06 103.709999 121.370003 142.160004 146.139999 159.179993 0.170283 0.370745 0.409122 0.534857
1982-08-13 103.849998 122.239998 139.529999 147.649994 163.699997 0.177082 0.343572 0.421762 0.576312
1982-08-20 113.019997 122.510002 137.020004 148.000000 164.339996 0.083967 0.212352 0.309503 0.454079
1982-08-27 117.110001 123.620003 134.880005 149.740005 162.250000 0.055589 0.151738 0.278627 0.385450
1984-03-09 154.350006 155.449997 155.169998 164.369995 178.789993 0.007127 0.005313 0.064917 0.158341
1984-04-13 157.309998 157.500000 150.880005 164.179993 180.919998 0.001208 -0.040875 0.043672 0.150086
2000-12-15 1312.150024 1326.650024 1150.530029 1214.359985 1134.359985 0.011051 -0.123172 -0.074527 -0.135495
2001-09-21 965.799988 1089.900024 1144.890015 1148.699951 833.700012 0.128495 0.185432 0.189377 -0.136778
2002-07-19 847.750000 950.700012 884.390015 901.780029 978.799988 0.121439 0.043220 0.063733 0.154586
2002-07-26 852.840027 947.950012 897.650024 861.400024 996.520020 0.111521 0.052542 0.010037 0.168472
SP400 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
1982-08-13 31.790001 36.830002 43.169998 46.270000 53.029999 0.158540 0.357974 0.455489 0.668135
1982-08-20 33.990002 36.980000 42.610001 46.639999 52.970001 0.087967 0.253604 0.372168 0.558399
1984-02-03 50.220001 48.720001 48.320000 48.330002 54.169998 -0.029869 -0.037834 -0.037634 0.078654
1984-02-24 47.849998 48.220001 45.639999 50.360001 54.880001 0.007733 -0.046186 0.052456 0.146918
1984-03-02 48.980000 48.389999 46.299999 50.220001 55.520000 -0.012046 -0.054716 0.025316 0.133524
1984-03-09 47.669998 47.119999 46.900002 49.730000 54.549999 -0.011538 -0.016153 0.043214 0.144326
1984-03-16 49.029999 48.150002 46.099998 50.450001 54.070000 -0.017948 -0.059759 0.028962 0.102794
1984-03-23 48.320000 47.340000 47.090000 50.369999 54.070000 -0.020281 -0.025455 0.042425 0.118998
1984-04-06 47.200001 48.279999 46.599998 48.220001 55.009998 0.022881 -0.012712 0.021610 0.165466
1984-04-13 47.570000 47.689999 46.090000 48.560001 55.959999 0.002523 -0.031112 0.020811 0.176372
1984-04-27 47.869999 45.330002 45.180000 49.430000 55.910000 -0.053060 -0.056194 0.032588 0.167955
1992-05-15 142.559998 140.570007 144.399994 152.729996 161.949997 -0.013959 0.012907 0.071338 0.136013
1994-12-09 162.660004 170.059998 179.259995 191.630005 217.550003 0.045494 0.102053 0.178102 0.337452
2002-07-05 478.579987 406.980011 389.079987 439.339996 494.779999 -0.149609 -0.187012 -0.081993 0.033850
2002-07-12 444.790009 436.589996 401.109985 443.450012 502.350006 -0.018436 -0.098204 -0.003013 0.129409
2002-07-19 421.510010 457.149994 422.980011 432.549988 488.049988 0.084553 0.003487 0.026191 0.157861
2002-07-26 416.339996 460.660004 428.630005 418.079987 496.559998 0.106451 0.029519 0.004179 0.192679
2002-08-02 419.279999 429.850006 433.059998 416.910004 490.230011 0.025210 0.032866 -0.005653 0.169219
2002-08-09 436.420013 444.200012 423.950012 403.480011 486.790009 0.017827 -0.028573 -0.075478 0.115416
SP600 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
1990-09-14 51.790001 44.759998 48.320000 59.830002 65.339996 -0.135741 -0.067001 0.155242 0.261633
1990-09-21 50.139999 45.279999 48.240002 59.959999 66.269997 -0.096929 -0.037894 0.195852 0.321699
1990-09-28 47.669998 44.549999 48.180000 61.369999 66.580002 -0.065450 0.010699 0.287393 0.396686
1990-10-05 47.470001 45.119999 48.020000 62.680000 65.459999 -0.049505 0.011586 0.320413 0.378976
1990-10-12 44.900002 45.860001 46.869999 63.380001 66.389999 0.021381 0.043875 0.411581 0.478619
1990-10-19 45.340000 46.400002 47.340000 63.759998 68.320000 0.023379 0.044111 0.406264 0.506837
1990-10-26 44.970001 46.060001 49.669998 62.750000 67.300003 0.024238 0.104514 0.395375 0.496553
1990-11-02 44.750000 47.570000 52.869999 62.939999 68.510002 0.063017 0.181452 0.406480 0.530950
1990-11-09 45.119999 48.709999 55.509998 62.930000 69.470001 0.079566 0.230275 0.394725 0.539672
1990-11-16 46.340000 47.919998 57.279999 61.349998 67.720001 0.034096 0.236081 0.323910 0.461372
1990-11-23 46.139999 48.189999 57.110001 61.959999 65.750000 0.044430 0.237755 0.342870 0.425011
1998-10-09 132.559998 165.919998 178.899994 160.789993 176.779999 0.251660 0.349578 0.212960 0.333585
2002-07-19 192.619995 206.380005 190.639999 197.020004 224.889999 0.071436 -0.010279 0.022843 0.167532
2002-07-26 192.940002 207.839996 194.509995 191.029999 231.419998 0.077226 0.008137 -0.009899 0.199440
2002-08-02 189.850006 194.160004 198.259995 189.750000 227.869995 0.022702 0.044298 -0.000527 0.200263
2002-08-09 197.960007 201.509995 194.479996 182.789993 226.539993 0.017933 -0.017579 -0.076632 0.144373
2002-08-16 200.330002 198.520004 197.039993 183.229996 236.869995 -0.009035 -0.016423 -0.085359 0.182399
2002-08-23 204.729996 184.899994 202.759995 185.119995 238.190002 -0.096859 -0.009622 -0.095785 0.163435
2002-08-30 200.259995 187.860001 203.649994 183.500000 247.570007 -0.061919 0.016928 -0.083691 0.236243
2018-12-07 905.260010 874.479980 934.179993 919.890015 999.059998 -0.034001 0.031947 0.016161 0.103617
2018-12-14 878.109985 894.059998 947.250000 923.570007 1007.929993 0.018164 0.078737 0.051770 0.147840
2018-12-21 809.609985 908.960022 918.090027 937.049988 1022.669983 0.122713 0.133990 0.157409 0.263164
2018-12-28 840.210022 917.070007 939.299988 953.250000 1018.020020 0.091477 0.117935 0.134538 0.211626
R1000 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
2001-09-21 507.980011 572.729980 602.700012 607.909973 442.309998 0.127466 0.186464 0.196720 -0.129277
2002-07-19 450.640015 504.040009 467.440002 477.609985 521.780029 0.118498 0.037280 0.059848 0.157864
2002-07-26 452.920013 502.829987 474.230011 456.950012 531.309998 0.110196 0.047050 0.008898 0.173077
R2000 returns over 1,3,6 and 12 months
Adj Close 30 91 182 365 30 chg 91 chg 182 chg 365 chg
Date
1990-09-21 133.740005 121.599998 130.850006 165.800003 178.800003 -0.090773 -0.021609 0.239719 0.336922
1998-10-09 318.399994 398.429993 431.230011 405.859985 430.190002 0.251351 0.354366 0.274686 0.351099
1998-10-16 342.869995 390.420013 427.049988 421.579987 408.899994 0.138682 0.245516 0.229562 0.192580
2000-12-15 458.029999 493.279999 441.799988 495.130005 479.940002 0.076960 -0.035434 0.080999 0.047835
2001-09-21 378.890015 430.500000 484.019989 502.390015 358.679993 0.136214 0.277468 0.325952 -0.053340
2002-07-19 386.200012 401.290009 363.369995 388.100006 457.170013 0.039073 -0.059114 0.004920 0.183765
2002-07-26 382.260010 407.730011 372.640015 375.059998 473.829987 0.066630 -0.025166 -0.018835 0.239549
2002-08-02 376.450012 379.130005 383.450012 372.170013 464.769989 0.007119 0.018595 -0.011369 0.234613
2002-08-09 388.450012 392.470001 379.000000 358.779999 459.269989 0.010349 -0.024327 -0.076381 0.182314
2018-12-21 1292.089966 1457.449951 1505.920044 1549.630005 1674.140015 0.127979 0.165492 0.199321 0.295684
2018-12-28 1337.920044 1473.540039 1539.739990 1566.569946 1664.150024 0.101366 0.150846 0.170900 0.243834
print(IndexComparisonDf)
Index 30 91 182 365 0 DJI 0.053810 0.076220 0.057901 0.077674 1 N100 0.005958 -0.137181 -0.068949 -0.033752 2 SP500 0.005369 0.023845 0.093902 0.209205 3 SP400 0.012233 0.007290 0.061636 0.196497 4 SP600 0.017999 0.074917 0.168865 0.310936 5 R1000 0.118720 0.090265 0.088489 0.067222 6 R2000 0.078632 0.095148 0.129043 0.204987
https://www.mybanktracker.com/blog/investing/average-stock-market-return-302399
Average Stock Market Returns (end date of September 19, 2019)
import pandas_datareader as pdr
from datetime import datetime, timedelta
sign = lambda b: (0 < b) - (0 > b)
df= pdr.get_data_yahoo("%5EGSPC", datetime(1936, 1, 1), datetime.now())["Adj Close"].to_frame()
window=70
downLimit=-16
df["Prolonged Liquidation"]= df['Adj Close'].pct_change().map(sign).replace(to_replace=0, method='ffill').rolling(window).sum()<=downLimit
df["weekday"]=[df.index[i].weekday() for i in range(len(df))]
aDf= df.loc[df["Prolonged Liquidation"] & (4==df["weekday"])] # 4 is Friday
print(aDf.to_string())
Adj Close Prolonged Liquidation weekday Date 1937-10-15 11.840000 True 4 1937-11-05 11.460000 True 4 1937-11-12 11.910000 True 4 1937-11-19 10.510000 True 4 1937-11-26 10.670000 True 4 1938-03-25 9.350000 True 4 1938-04-01 8.910000 True 4 1938-04-08 9.620000 True 4 1941-10-31 9.500000 True 4 1941-12-12 8.730000 True 4 1941-12-19 8.490000 True 4 1941-12-26 8.420000 True 4 1942-01-02 8.890000 True 4 1942-01-09 8.850000 True 4 1942-01-16 8.960000 True 4 1942-01-23 8.840000 True 4 1942-01-30 8.850000 True 4 1942-02-06 8.850000 True 4 1942-02-13 8.620000 True 4 1942-02-20 8.470000 True 4 1942-02-27 8.590000 True 4 1942-03-06 8.160000 True 4 1942-03-13 8.030000 True 4 1942-03-20 8.110000 True 4 1942-03-27 8.050000 True 4 1942-04-17 7.700000 True 4 1942-04-24 7.550000 True 4 1946-11-22 14.130000 True 4 1953-06-19 23.840000 True 4 1956-10-26 46.270000 True 4 1956-11-02 46.980000 True 4 1956-11-09 46.340000 True 4 1956-11-16 45.740002 True 4 1956-11-23 45.139999 True 4 1956-11-30 45.080002 True 4 1956-12-14 46.540001 True 4 1957-10-25 40.590000 True 4 1957-11-01 40.439999 True 4 1962-05-11 62.650002 True 4 1962-05-18 63.820000 True 4 1962-05-25 59.470001 True 4 1962-06-01 59.380001 True 4 1962-06-08 58.450001 True 4 1962-06-15 55.889999 True 4 1962-06-22 52.680000 True 4 1962-06-29 54.750000 True 4 1962-07-06 56.169998 True 4 1962-07-13 57.830002 True 4 1962-07-20 56.810001 True 4 1966-10-14 76.599998 True 4 1970-02-20 88.029999 True 4 1970-04-17 85.669998 True 4 1970-04-24 82.769997 True 4 1970-05-01 81.440002 True 4 1970-05-08 79.440002 True 4 1970-05-15 76.900002 True 4 1970-05-22 72.250000 True 4 1970-05-29 76.550003 True 4 1970-06-05 76.169998 True 4 1970-06-12 73.879997 True 4 1970-06-19 77.050003 True 4 1970-06-26 73.470001 True 4 1970-07-10 74.449997 True 4 1970-07-17 77.690002 True 4 1974-02-01 95.320000 True 4 1974-02-08 92.330002 True 4 1974-08-16 75.669998 True 4 1974-08-23 71.550003 True 4 1974-08-30 72.150002 True 4 1974-09-06 71.419998 True 4 1974-09-13 65.199997 True 4 1974-09-20 70.139999 True 4 1974-09-27 64.940002 True 4 1974-10-04 62.340000 True 4 1974-10-11 71.139999 True 4 1974-10-25 70.120003 True 4 1974-11-01 73.879997 True 4 1974-11-08 74.910004 True 4 1974-11-15 71.910004 True 4 1974-11-22 68.900002 True 4 1982-07-30 107.089996 True 4 1982-08-06 103.709999 True 4 1982-08-13 103.849998 True 4 1982-08-20 113.019997 True 4 1982-08-27 117.110001 True 4 1984-03-09 154.350006 True 4 1984-04-06 155.479996 True 4 1984-04-13 157.309998 True 4 2000-11-17 1367.719971 True 4 2000-11-24 1341.770020 True 4 2000-12-01 1315.229980 True 4 2000-12-15 1312.150024 True 4 2001-09-21 965.799988 True 4 2002-07-12 921.390015 True 4 2002-07-19 847.750000 True 4 2002-07-26 852.840027 True 4 2002-08-02 864.239990 True 4 2018-12-28 2485.739990 True 4 2022-10-14 3583.070068 True 4 2022-11-04 3732.229980 True 4
df= pdr.get_data_yahoo("%5EGSPC", datetime(1936, 1, 1), datetime.now())["Adj Close"].to_frame()
window=70
downLimit=-16
df["ffill"]= df['Adj Close'].pct_change().map(sign).replace(to_replace=0, method='ffill')
df["rollingSum"]= df['ffill'].rolling(window=window).sum()
df["downLimit"]= df['rollingSum']<=downLimit
print(df.iloc[-80:].to_string())
Adj Close ffill rollingSum downLimit Date 2022-07-15 3863.159912 1 -10.0 False 2022-07-18 3830.850098 -1 -10.0 False 2022-07-19 3936.689941 1 -8.0 False 2022-07-20 3959.899902 1 -8.0 False 2022-07-21 3998.949951 1 -6.0 False 2022-07-22 3961.629883 -1 -6.0 False 2022-07-25 3966.840088 1 -4.0 False 2022-07-26 3921.050049 -1 -6.0 False 2022-07-27 4023.610107 1 -4.0 False 2022-07-28 4072.429932 1 -2.0 False 2022-07-29 4130.290039 1 -2.0 False 2022-08-01 4118.629883 -1 -2.0 False 2022-08-02 4091.189941 -1 -2.0 False 2022-08-03 4155.169922 1 0.0 False 2022-08-04 4151.939941 -1 -2.0 False 2022-08-05 4145.189941 -1 -2.0 False 2022-08-08 4140.060059 -1 -4.0 False 2022-08-09 4122.470215 -1 -6.0 False 2022-08-10 4210.240234 1 -4.0 False 2022-08-11 4207.270020 -1 -6.0 False 2022-08-12 4280.149902 1 -6.0 False 2022-08-15 4297.140137 1 -6.0 False 2022-08-16 4305.200195 1 -4.0 False 2022-08-17 4274.040039 -1 -4.0 False 2022-08-18 4283.740234 1 -2.0 False 2022-08-19 4228.479980 -1 -4.0 False 2022-08-22 4137.990234 -1 -4.0 False 2022-08-23 4128.729980 -1 -4.0 False 2022-08-24 4140.770020 1 -4.0 False 2022-08-25 4199.120117 1 -2.0 False 2022-08-26 4057.659912 -1 -4.0 False 2022-08-29 4030.610107 -1 -4.0 False 2022-08-30 3986.159912 -1 -4.0 False 2022-08-31 3955.000000 -1 -6.0 False 2022-09-01 3966.850098 1 -6.0 False 2022-09-02 3924.260010 -1 -6.0 False 2022-09-06 3908.189941 -1 -8.0 False 2022-09-07 3979.870117 1 -8.0 False 2022-09-08 4006.179932 1 -8.0 False 2022-09-09 4067.360107 1 -6.0 False 2022-09-12 4110.410156 1 -4.0 False 2022-09-13 3932.689941 -1 -6.0 False 2022-09-14 3946.010010 1 -4.0 False 2022-09-15 3901.350098 -1 -6.0 False 2022-09-16 3873.330078 -1 -8.0 False 2022-09-19 3899.889893 1 -6.0 False 2022-09-20 3855.929932 -1 -6.0 False 2022-09-21 3789.929932 -1 -6.0 False 2022-09-22 3757.989990 -1 -6.0 False 2022-09-23 3693.229980 -1 -6.0 False 2022-09-26 3655.040039 -1 -8.0 False 2022-09-27 3647.290039 -1 -8.0 False 2022-09-28 3719.040039 1 -8.0 False 2022-09-29 3640.469971 -1 -10.0 False 2022-09-30 3585.620117 -1 -10.0 False 2022-10-03 3678.429932 1 -10.0 False 2022-10-04 3790.929932 1 -10.0 False 2022-10-05 3783.280029 -1 -10.0 False 2022-10-06 3744.520020 -1 -10.0 False 2022-10-07 3639.659912 -1 -10.0 False 2022-10-10 3612.389893 -1 -10.0 False 2022-10-11 3588.840088 -1 -12.0 False 2022-10-12 3577.030029 -1 -14.0 False 2022-10-13 3669.909912 1 -14.0 False 2022-10-14 3583.070068 -1 -16.0 True 2022-10-17 3677.949951 1 -14.0 False 2022-10-18 3719.979980 1 -12.0 False 2022-10-19 3695.159912 -1 -12.0 False 2022-10-20 3665.780029 -1 -12.0 False 2022-10-21 3752.750000 1 -10.0 False 2022-10-24 3797.340088 1 -10.0 False 2022-10-25 3859.110107 1 -8.0 False 2022-10-26 3830.600098 -1 -10.0 False 2022-10-27 3807.300049 -1 -12.0 False 2022-10-28 3901.060059 1 -12.0 False 2022-10-31 3871.979980 -1 -12.0 False 2022-11-01 3856.100098 -1 -14.0 False 2022-11-02 3759.689941 -1 -14.0 False 2022-11-03 3719.889893 -1 -16.0 True 2022-11-04 3770.550049 1 -16.0 True