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Refactor Technical Analysis according to pandas_ta repo

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didier
2020-12-26 19:31:48 +00:00
parent 51b18d92bd
commit 7b596b4fdb
2 changed files with 97 additions and 51 deletions
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8
stock_market_cli.py
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@@ -69,6 +69,7 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
print(" rsi relative strength index")
print(" adx average directional movement index")
print(" cci commodity channel index")
print(" aroon aroon indicator")
if s_interval != "1440min":
print(" vwap volume weighted average price")
@@ -115,7 +116,7 @@ def main():
# Add list of arguments that the main parser accepts
main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers' ,'view', 'load', 'clear',
'sma', 'ema', 'macd', 'vwap', 'stoch', 'rsi', 'adx',
'cci', 'ratings'])
'cci', 'aroon', 'ratings'])
# Print first welcome message and help
print("\nWelcome to Didier's Stock Market Bot\n")
@@ -223,6 +224,11 @@ def main():
smta.cci(l_args, s_ticker, s_interval, df_stock)
continue
# --------------------------------------------------- AROON ---------------------------------------------------
elif ns_known_args.cmd == 'aroon':
smta.aroon(l_args, s_ticker, s_interval, df_stock)
continue
# --------------------------------------------------------------------------------------------------------------
# ------------------------------------------------ PREDICTION --------------------------------------------------
# --------------------------------------------------------------------------------------------------------------

140
stock_market_technical_analysis.py
View File

@@ -15,8 +15,9 @@ def sma(l_args, s_ticker, s_interval, df_stock):
equal weight, and values outside of the time period are not included in the average.
This makes it less responsive to recent changes in the data, which can be useful for
filtering out those changes. """)
parser.add_argument('-p', "--period", action="store", dest="n_time_period", type=check_positive, default=20,
help='Number of data points used to calculate each moving average value.')
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -30,12 +31,12 @@ def sma(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.sma(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} SMA")
df_ta = ta.sma(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "SMA")
# Intraday
else:
df_ta = ta.sma(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{ns_parser.n_time_period} SMA")
df_ta = ta.sma(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "SMA")
except:
print("")
return
@@ -52,8 +53,9 @@ def ema(l_args, s_ticker, s_interval, df_stock):
a diminishing contribution to the average, while more recent values have a greater
contribution. This method allows the moving average to be more responsive to changes
in the data. """)
parser.add_argument('-p', "--period", action="store", dest="n_time_period", type=check_positive, default=20,
help='Number of data points used to calculate each moving average value.')
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -67,12 +69,12 @@ def ema(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.ema(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} EMA")
df_ta = ta.ema(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "EMA")
# Intraday
else:
df_ta = ta.ema(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{ns_parser.n_time_period} EMA")
df_ta = ta.ema(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "EMA")
except:
print("")
return
@@ -112,14 +114,12 @@ def macd(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.macd(df_stock['5. adjusted close'],
fast=ns_parser.n_fast, slow=ns_parser.n_slow,
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
df_ta = ta.macd(df_stock['5. adjusted close'], fast=ns_parser.n_fast, slow=ns_parser.n_slow,
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
# Intraday
else:
df_ta = ta.macd(df_stock['4. close'],
fast=ns_parser.n_fast, slow=ns_parser.n_slow,
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
df_ta = ta.macd(df_stock['4. close'], fast=ns_parser.n_fast, slow=ns_parser.n_slow,
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, f"{ns_parser.n_fast}-{ns_parser.n_slow}-{ns_parser.n_signal}-{ns_parser.n_offset} MACD")
except:
@@ -133,6 +133,8 @@ def vwap(l_args, s_ticker, s_interval, df_stock):
description=""" The Volume Weighted Average Price that measures the average typical price
by volume. It is typically used with intraday charts to identify general direction. """)
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
@@ -143,8 +145,10 @@ def vwap(l_args, s_ticker, s_interval, df_stock):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'], volume=df_stock['5. volume'], interval=s_interval)
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{s_interval} VWAP")
df_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
volume=df_stock['5. volume'], offset=ns_parser.n_offset)
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "VWAP")
except:
print("")
return
@@ -160,16 +164,13 @@ def stoch(l_args, s_ticker, s_interval, df_stock):
below, it is a sell signal. The Raw %K is generally considered too erratic to use
for crossover signals. """)
parser.add_argument('-k', "--fastkperiod", action="store", dest="n_fastkperiod", type=check_positive, default=5,
parser.add_argument('-k', "--fastkperiod", action="store", dest="n_fastkperiod", type=check_positive, default=14,
help='The time period of the fastk moving average')
parser.add_argument('-d', "--slowdperiod", action="store", dest="n_slowdperiod", type=check_positive, default=3,
help='TThe time period of the slowd moving average')
parser.add_argument("--slowkperiod", action="store", dest="n_slowkperiod", type=check_positive, default=3,
help='The time period of the slowk moving average')
parser.add_argument("--slowkmatype", action="store", dest="n_slowkmatype", type=check_positive, default=0,
help='Moving average type for the slowk moving average')
parser.add_argument("--slowdmatype", action="store", dest="n_slowdmatype", type=check_positive, default=0,
help='Moving average type for the slowd moving average')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -181,12 +182,8 @@ def stoch(l_args, s_ticker, s_interval, df_stock):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ta = ta.stoch(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['4. close'],
k=ns_parser.n_fastkperiod,
d=ns_parser.n_slowdperiod,
smooth_k=ns_parser.n_slowkperiod).dropna()
df_ta = ta.stoch(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'], k=ns_parser.n_fastkperiod,
d=ns_parser.n_slowdperiod, smooth_k=ns_parser.n_slowkperiod, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, f"SlowK{ns_parser.n_slowkperiod}-SlowD{ns_parser.n_slowdperiod} STOCH")
except:
@@ -203,8 +200,10 @@ def rsi(l_args, s_ticker, s_interval, df_stock):
the value is over 70/below 30. You can also look for divergence with price. If
the price is making new highs/lows, and the RSI is not, it indicates a reversal. """)
parser.add_argument('-p', "--timeperiod", action="store", dest="n_timeperiod", type=check_positive, default=60,
help='Number of data points used to calculate each RSI value')
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=100, help='scalar')
parser.add_argument('-d', "--drift", action="store", dest="n_drift", type=check_positive, default=0, help='drift')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -218,12 +217,14 @@ def rsi(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.rsi(df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.rsi(df_stock['5. adjusted close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
# Intraday
else:
df_ta = ta.rsi(df_stock['4. close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.rsi(df_stock['4. close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, f"{ns_parser.n_timeperiod} RSI")
plot_ta(s_ticker, df_ta, "RSI")
except:
print("")
return
@@ -237,8 +238,10 @@ def adx(l_args, s_ticker, s_interval, df_stock):
To interpret the ADX, consider a high number to be a strong trend, and a low number,
a weak trend. """)
parser.add_argument('-p', "--timeperiod", action="store", dest="n_timeperiod", type=check_positive, default=60,
help='Number of data points used to calculate each ADX value')
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=100, help='scalar')
parser.add_argument('-d', "--drift", action="store", dest="n_drift", type=check_positive, default=0, help='drift')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -252,14 +255,14 @@ def adx(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.adx(high=df_stock['2. high'], low=df_stock['3. low'],
close=df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.adx(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'], length=ns_parser.n_length,
scalar=ns_parser.n_scalar, drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
# Intraday
else:
df_ta = ta.adx(high=df_stock['2. high'], low=df_stock['3. low'],
close=df_stock['4. close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.adx(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'], length=ns_parser.n_length,
scalar=ns_parser.n_scalar, drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, f"{ns_parser.n_timeperiod} ADX")
plot_ta(s_ticker, df_ta, "ADX")
except:
print("")
return
@@ -274,8 +277,9 @@ def cci(l_args, s_ticker, s_interval, df_stock):
divergence in the CCI. If the price is making new highs, and the CCI is not,
then a price correction is likely. """)
parser.add_argument('-p', "--timeperiod", action="store", dest="n_timeperiod", type=check_positive, default=60,
help='Number of data points used to calculate each ADX value')
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=0.015, help='scalar')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -289,14 +293,50 @@ def cci(l_args, s_ticker, s_interval, df_stock):
try:
# Daily
if s_interval == "1440min":
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'],
close=df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
# Intraday
else:
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'],
close=df_stock['4. close'], time_period=ns_parser.n_timeperiod).dropna()
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, f"{ns_parser.n_timeperiod} CCI")
plot_ta(s_ticker, df_ta, "CCI")
except:
print("")
return
# ----------------------------------------------------- AROON -----------------------------------------------------
def aroon(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='aroon',
description=""" The word aroon is Sanskrit for "dawn's early light." The Aroon
indicator attempts to show when a new trend is dawning. The indicator consists
of two lines (Up and Down) that measure how long it has been since the highest
high/lowest low has occurred within an n period range. \n \n When the Aroon Up is
staying between 70 and 100 then it indicates an upward trend. When the Aroon Down
is staying between 70 and 100 then it indicates an downward trend. A strong upward
trend is indicated when the Aroon Up is above 70 while the Aroon Down is below 30.
Likewise, a strong downward trend is indicated when the Aroon Down is above 70 while
the Aroon Up is below 30. Also look for crossovers. When the Aroon Down crosses above
the Aroon Up, it indicates a weakening of the upward trend (and vice versa). """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=100, help='scalar')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ta = ta.aroon(high=df_stock['2. high'], low=df_stock['3. low'], length=ns_parser.n_length,
scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_ta(s_ticker, df_ta, "AROON")
except:
print("")
return