def indicator_adder(df):
'''
this adds the most common indicators to a dataframe
with lower case close open high low volume
takes: dataframe
returns : dataframe
'''
df['rsi'] = pta.rsi(df.close)
df['riz'] = pta.rsi(df.close,length=2)
df['vwap'] = pta.vwap(df.high,df.low,df.close,df.volume)
df[['stoch_k','stoch_d']] = pta.stoch(df.high,df.low,df.close)
if len(df)>20:
df['ema'] = pta.ema(df.close,length=20)
#if len(df)>6:
#df = super_trend(df)
return df
def vwap(day_df: pd.DataFrame, offset: int) -> pd.DataFrame:
"""Gets volume weighted average price (VWAP)
Parameters
----------
day_df: pd.DataFrame
Dataframe of dates and prices for the last trading day
offset: int
Length of offset
Returns
----------
df_vwap: pd.DataFrame
Dataframe with VWAP data
"""
df_vwap = ta.vwap(
high=day_df["High"],
low=day_df["Low"],
close=day_df["Close"],
volume=day_df["Volume"],
offset=offset,
)
return pd.DataFrame(df_vwap)
def vwap(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
add_help=False,
prog="vwap",
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 = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
# Daily
if s_interval == "1440min":
df_ta = ta.vwap(
high=df_stock["2. high"],
low=df_stock["3. low"],
close=df_stock["5. adjusted close"],
volume=df_stock["6. volume"],
offset=ns_parser.n_offset,
)
# Intraday
else:
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,
)
_, axPrice = plt.subplots()
if s_interval == "1440min":
plt.plot(df_stock.index,
df_stock["5. adjusted close"].values,
color="k")
else:
plt.plot(df_stock.index, df_stock["4. close"].values, color="k")
plt.plot(df_ta.index, df_ta.values)
plt.title(f"VWAP on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.legend([s_ticker, "VWAP"])
_ = axPrice.twinx()
if s_interval == "1440min":
plt.bar(
df_stock.index,
df_stock["6. volume"].values,
color="k",
alpha=0.8,
width=0.3,
)
else:
plt.bar(
df_stock.index,
df_stock["5. volume"].values,
color="k",
alpha=0.8,
width=0.3,
)
plt.ylabel("Volume")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
plt.ion()
plt.show()
print("")
except Exception as e:
print(e)
print("")
def add_plots(df: pd.DataFrame, additional_charts: Dict[str, bool]):
"""Add additional plots to the candle chart.
Parameters
----------
df : pd.DataFrame
The source data
additional_charts : Dict[str, bool]
A dictionary of flags to include additional charts
Returns
-------
Tuple
Tuple of lists containing the plots, legends and subplot legends
"""
panel_number = 2
plots_to_add = []
legends = []
subplot_legends = []
if additional_charts["ad"]:
ad = ta.ad(df["High"], df["Low"], df["Close"], df["Volume"])
ad_plot = mpf.make_addplot(ad, panel=panel_number)
plots_to_add.append(ad_plot)
subplot_legends.extend([panel_number * 2, ["AD"]])
panel_number += 1
if additional_charts["bbands"]:
bbands = ta.bbands(df["Close"])
bbands = bbands.drop("BBB_5_2.0", axis=1)
bbands_plot = mpf.make_addplot(bbands, panel=0)
plots_to_add.append(bbands_plot)
legends.extend(["Lower BBand", "Middle BBand", "Upper BBand"])
if additional_charts["cci"]:
cci = ta.cci(df["High"], df["Low"], df["Close"])
cci_plot = mpf.make_addplot(cci, panel=panel_number)
plots_to_add.append(cci_plot)
subplot_legends.extend([panel_number * 2, ["CCI"]])
panel_number += 1
if additional_charts["ema"]:
ema = ta.ema(df["Close"])
ema_plot = mpf.make_addplot(ema, panel=0)
plots_to_add.append(ema_plot)
legends.append("10 EMA")
if additional_charts["rsi"]:
rsi = ta.rsi(df["Close"])
rsi_plot = mpf.make_addplot(rsi, panel=panel_number)
plots_to_add.append(rsi_plot)
subplot_legends.extend([panel_number * 2, ["RSI"]])
panel_number += 1
if additional_charts["obv"]:
obv = ta.obv(df["Close"], df["Volume"])
obv_plot = mpf.make_addplot(obv, panel=panel_number)
plots_to_add.append(obv_plot)
subplot_legends.extend([panel_number * 2, ["OBV"]])
panel_number += 1
if additional_charts["sma"]:
sma_length = [20, 50]
for length in sma_length:
sma = ta.sma(df["Close"], length=length)
sma_plot = mpf.make_addplot(sma, panel=0)
plots_to_add.append(sma_plot)
legends.append(f"{length} SMA")
if additional_charts["vwap"]:
vwap = ta.vwap(df["High"], df["Low"], df["Close"], df["Volume"])
vwap_plot = mpf.make_addplot(vwap, panel=0)
plots_to_add.append(vwap_plot)
legends.append("vwap")
return plots_to_add, legends, subplot_legends
def integrate_with_indicators_forecasting(input_path, output_path, start_date,
test_set, end_date_for_clustering):
folder_creator(PATH_INTEGRATED_FOLDER, 1)
for crypto in os.listdir(input_path):
for date_to_predict in test_set:
#end_date=pd.to_datetime(date_to_predict) - timedelta(days=1)
end_date = date_to_predict
df = cut_dataset_by_range(input_path,
crypto.replace(".csv", ""),
start_date,
end_date,
features_to_use=None)
df["Date"] = pd.to_datetime(df["Date"])
day_to_predict = df.loc[len(df.Date) - 1]
df = df[:-1] #remove the day to predict
#df = df.sort_values('Date', ascending=True)
data_series_of_target_feature = df[TARGET_FEATURE]
for lookback_value in LOOKBACK_TEST:
df['VWAP'] = panda.vwap(df['High'],
df['Low'],
df['Close'],
df['Volume'],
lookback_value=lookback_value)
for lookback_value in LOOKBACK_TEST:
df[str('SMA_' + str(lookback_value))] = get_SMA(
data_series_of_target_feature, lookback_value)
for lookback_value in LOOKBACK_TEST:
df[str('EMA_' + str(lookback_value))] = get_EMA(
data_series_of_target_feature, lookback_value)
for lookback_value in LOOKBACK_TEST:
df[str('RSI_' + str(lookback_value))] = get_RSI(
data_series_of_target_feature, lookback_value)
df_macd = get_MACD(data_series_of_target_feature)
df['MACD_12_26_9'] = df_macd['MACD_12_26_9']
df['MACDH_12_26_9'] = df_macd['MACDH_12_26_9']
df['MACDS_12_26_9'] = df_macd['MACDS_12_26_9']
df_bbs = panda.bbands(data_series_of_target_feature)
df['BBL_20'] = df_bbs['BBL_20']
df['BBM_20'] = df_bbs['BBM_20']
df['BBU_20'] = df_bbs['BBU_20']
df['MOM'] = panda.mom(data_series_of_target_feature)
df_stoch = panda.stoch(df['High'], df['Low'], df['Close'])
df['STOCHF_14'] = df_stoch['STOCHF_14']
df['STOCHF_3'] = df_stoch['STOCHF_3']
df['STOCH_5'] = df_stoch['STOCH_5']
df['STOCH_3'] = df_stoch['STOCH_3']
df['CMO'] = panda.cmo(data_series_of_target_feature)
df['DPO'] = panda.dpo(data_series_of_target_feature)
df['UO'] = panda.uo(df['High'], df['Low'], df['Close'])
df['lag_1'] = df['Close'].shift(1)
"""df['lag_7'] = df['Close'].shift(7)
df = df.iloc[7:]"""
df = df.append(day_to_predict, ignore_index=True)
df.fillna(value=0, inplace=True)
df.to_csv(os.path.join(
output_path,
crypto.replace(".csv", "") + str("_" + date_to_predict) +
".csv"),
sep=",",
index=False)
def add_plots(df, ns_parser):
panel_number = 2
plots_to_add = []
legends = []
subplot_legends = []
if ns_parser.ad:
ad = ta.ad(df["High"], df["Low"], df["Close"], df["Volume"])
ad_plot = mpf.make_addplot(ad, panel=panel_number)
plots_to_add.append(ad_plot)
subplot_legends.extend([panel_number * 2, ["AD"]])
panel_number += 1
if ns_parser.bbands:
bbands = ta.bbands(df["Close"])
bbands = bbands.drop("BBB_5_2.0", axis=1)
bbands_plot = mpf.make_addplot(bbands, panel=0)
plots_to_add.append(bbands_plot)
legends.extend(["Lower BBand", "Middle BBand", "Upper BBand"])
if ns_parser.cci:
cci = ta.cci(df["High"], df["Low"], df["Close"])
cci_plot = mpf.make_addplot(cci, panel=panel_number)
plots_to_add.append(cci_plot)
subplot_legends.extend([panel_number * 2, ["CCI"]])
panel_number += 1
if ns_parser.ema:
ema = ta.ema(df["Close"])
ema_plot = mpf.make_addplot(ema, panel=0)
plots_to_add.append(ema_plot)
legends.append("10 EMA")
if ns_parser.rsi:
rsi = ta.rsi(df["Close"])
rsi_plot = mpf.make_addplot(rsi, panel=panel_number)
plots_to_add.append(rsi_plot)
subplot_legends.extend([panel_number * 2, ["RSI"]])
panel_number += 1
if ns_parser.obv:
obv = ta.obv(df["Close"], df["Volume"])
obv_plot = mpf.make_addplot(obv, panel=panel_number)
plots_to_add.append(obv_plot)
subplot_legends.extend([panel_number * 2, ["OBV"]])
panel_number += 1
if ns_parser.sma:
sma_length = [20, 50]
for length in sma_length:
sma = ta.sma(df["Close"], length=length)
sma_plot = mpf.make_addplot(sma, panel=0)
plots_to_add.append(sma_plot)
legends.append(f"{length} SMA")
if ns_parser.vwap:
vwap = ta.vwap(df["High"], df["Low"], df["Close"], df["Volume"])
vwap_plot = mpf.make_addplot(vwap, panel=0)
plots_to_add.append(vwap_plot)
legends.append("vwap")
return plots_to_add, legends, subplot_legends
sns.set()
initial = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&interval=1min&symbol=msft&apikey=OUMVBY0VK0HS8I9E&datatype=csv&outputsize=full'
)
initial = initial[::-1].reset_index(drop=True)
volume = initial['volume']
initial.drop(['volume', 'timestamp'], axis=1, inplace=True)
bbands = ta.bbands(initial['close'], length=50, std=2) #calculating indicators
rsi = ta.rsi(initial['close'], length=20)
ema_50 = ta.ema(initial['close'], length=5)
ema_200 = ta.ema(initial['close'], length=20)
ema_500 = ta.ema(initial['close'], length=50)
#macd = ta.macd(initial['close'], 5, 35, 5)
vwap = ta.vwap(initial['high'], initial['low'], initial['close'], volume)
initial = pd.concat([initial, bbands, ema_50, ema_200, ema_500, vwap], axis=1)
initial.columns = [
'open', 'high', 'low', 'close', 'bband1', 'useless', 'bband2', 'ema1',
'ema2', 'ema3', 'vwap'
]
initialInvestment = 1000
b = backtester.Backtester(initialInvestment)
initial = initial.dropna()
X = initial[:-1]
y = initial['close'].shift(-1)
X = X.dropna()
y = y.dropna()
X_train, X_test, y_train, y_test = train_test_split(X,
def vwap(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
prog='vwap',
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)
if l_unknown_args:
print(
f"The following args couldn't be interpreted: {l_unknown_args}\n"
)
return
# Daily
if s_interval == "1440min":
df_ta = ta.vwap(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['5. adjusted close'],
volume=df_stock['6. volume'],
offset=ns_parser.n_offset)
pfig, axPrice = plt.subplots()
plt.plot(df_stock.index,
df_stock['5. adjusted close'].values,
color='k')
plt.plot(df_ta.index, df_ta.values)
plt.title(f"VWAP on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.xlabel('Time')
plt.ylabel('Share Price ($)')
plt.legend([s_ticker, "VWAP"])
axVolume = axPrice.twinx()
plt.bar(df_stock.index,
df_stock['6. volume'].values,
color='k',
alpha=0.8,
width=.3)
plt.ylabel('Volume')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
plt.show()
print("")
# Intraday
else:
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)
pfig, axPrice = plt.subplots()
plt.plot(df_stock.index, df_stock['4. close'].values, color='k')
plt.plot(df_ta.index, df_ta.values)
plt.title(f"VWAP on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.xlabel('Time')
plt.ylabel('Share Price ($)')
plt.legend([s_ticker, "VWAP"])
axVolume = axPrice.twinx()
plt.bar(df_stock.index,
df_stock['5. volume'].values,
color='k',
alpha=0.8,
width=.3)
plt.ylabel('Volume')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
plt.show()
print("")
except:
print("")