def wma20_macd_system(price_data, wma_period1=20,period="", trade_type="Both_leg", underlying_instrument_data=None):
wma_period1 = wma_period1
period_wma = str(wma_period1) + "_wma"
if period=="":
price_period=price_data
else:
price_period=series_resampling.price_series_periodic(price_data,period)
wma_1.wma(price_period, wma_period1)
macd_1.macd(price_period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = np.where((price_signal_period[period_wma] <= price_signal_period["Close"]) &
(price_signal_period["macd_diff"] > 0), 1,
np.where((price_signal_period[period_wma] > price_signal_period["Close"]) &
(price_signal_period["macd_diff"] < 0), -1, 0))
price_signal["Signal"] = price_signal_period["Signal"].resample("D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def bband_system(price_data, period1=10, period="", trade_type="Both_leg", underlying_instrument_data=None):
period_sma = str(period1) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(price_data, period)
bollinger_percentage.bollinger_percentage(price_period, period1)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = np.where((price_signal_period["Bollinger percentage"] > 1), 1,
np.where((price_signal_period["Bollinger percentage"] < -1), -1,
np.nan))
price_signal_period["Signal"].fillna(method="ffill",inplace=True)
price_signal["Signal"] = price_signal_period["Signal"].resample("D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def periodic_reversal_system(price_data,
period_1=14,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal = price_data
price_signal_period = price_period
price_signal_period["Signal"] = signal_generation(
price_signal_period.index, period_1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def duration_system(price_data,
period=100,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period_duration = str(period) + "_duration"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = np.where(
(price_signal_period["Close"] > price_signal_period[period_sma]), 1,
-1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def technical_setups_system(price_data,indicator="bullish_engulfing", period1=20, period="",trade_type="Both_leg", underlying_instrument_data=None):
if period=="":
price_period=price_data
else:
price_period=series_resampling.price_series_periodic(price_data,period)
method_name = indicator
method = eval(method_name)
strategy_func=getattr(method,indicator)
strategy_func(price_period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = np.where(price_signal_period["Indicator"].rolling(period1).sum()>0,1,0)
price_signal_period["Signal"]=price_signal_period["Signal"].shift()
price_signal["Signal"] = price_signal_period["Signal"].resample("D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def oscillator_system(price_data,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
oscillator.oscillator(price_period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = price_signal_period["Oscillator"].apply(
signal_generator)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"] = price_signal["Signal"].shift(1)
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal["Trades"].fillna(0, inplace=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Open"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Open")
return trades, price_signal
def vortex_system(price_data, period="", trade_type="Both_leg", underlying_instrument_data=None):
if period=="":
price_period=price_data
else:
price_period=series_resampling.price_series_periodic(price_data,period)
price_signal = price_data
price_signal_period = price_period
vortex.vortex(price_period)
price_signal_period["Signal"] = np.where((price_signal_period["Vortex"]>0), 1, -1)
price_signal["Signal"] = price_signal_period["Signal"].resample("D").ffill()
price_signal["Signal"] = price_signal["Signal"].shift(1)
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal["Trades"].fillna(0,inplace=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(price_signal, price_signal["Open"],
underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Open")
return trades,price_signal
def pattern_system(price_data,
atr_period=44,
daily_range_period=4,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["ATR"] = atr.atr(price_signal_period, atr_period)
price_signal_period["4 DR"] = price_signal_period["Close"].rolling(
daily_range_period).max() - price_signal_period["Close"].rolling(
daily_range_period).min()
price_signal_period["Trading Window"] = np.where(
(price_signal_period["4 DR"] < price_signal_period["ATR"]), 1, 0)
price_signal_period["Trading Window"] = price_signal_period[
"Trading Window"].shift(1)
price_signal_period["Buy Level"] = price_signal_period["Open"] + 0.62 * (
price_signal_period["High"].shift(1) -
price_signal_period["Low"].shift(1))
price_signal_period["Sell Level"] = price_signal_period["Open"] - 0.62 * (
price_signal_period["High"].shift(1) -
price_signal_period["Low"].shift(1))
Signal = price_signal_period.apply(lambda x: signal_generation(*x), axis=1)
Signal = Signal.apply(pd.Series)
Signal.columns = ["Signal", "Trade_Level"]
price_signal_period = pd.concat([price_signal_period, Signal], axis=1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trade_Level"] = price_signal_period["Trade_Level"]
price_signal["Trade_Level"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Trade_Level"],
underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Trade_Level")
return trades, price_signal
def regression_crossover_system(price_data,
period1=10,
period2=5,
period3=50,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period_sma = str(period1) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
sma.sma(price_period, period1)
price_signal_period = price_period
price_signal = price_data
price_signal_period["row_number"] = np.arange(1,
len(price_signal_period) + 1)
price_signal_period["d5_LS"] = rolling_apply(
get_slope, period2, price_signal_period["Close"],
price_signal_period["row_number"])
price_signal_period["d5_LS"].fillna(0, inplace=True)
price_signal_period["d50_LR"] = rolling_apply(
regress_predict, period3, price_signal_period["row_number"],
price_signal_period["d5_LS"])
price_signal_period["d50_LR"].fillna(0)
price_signal_period["Signal"] = np.where(
(price_signal_period["Close"] > price_signal_period[period_sma]) &
(price_signal_period["d5_LS"] > price_signal_period["d50_LR"]), 1,
np.where(
(price_signal_period["Close"] < price_signal_period[period_sma]) &
(price_signal_period["d5_LS"] < price_signal_period["d50_LR"]), -1,
0))
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def body_outside_band_system(price_data,
period1=20,
bandwidth=1,
profit_target=6,
trailing_stop=2,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
trailing_stop = -trailing_stop / 100
profit_target = profit_target / 100
bandwidth = bandwidth / 100
period_ema = str(period1) + "_EMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
ema.ema(price_signal_period, period1)
price_signal_period["Upper_band"] = price_signal_period[period_ema] * (
1 + (bandwidth))
price_signal_period["Lower_band"] = price_signal_period[period_ema] * (
1 - (bandwidth))
price_signal_period["Trade_signal"] = np.where(
price_signal_period["Low"] > price_signal_period["Upper_band"].shift(),
1,
np.where(
price_signal_period["High"] <
price_signal_period["Lower_band"].shift(), -1, 0))
price_signal_period["Signal"] = 0
signal_generation(price_signal_period, profit_target, trailing_stop)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Close"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def rsi2sma50sl2_system(price_data,
period_sma=50,
period_rsi=2,
stop_level=2,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
stop_level = stop_level / 100
period_sma_str = str(period_sma) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
sma.sma(price_period, period_sma)
rsi.rsi(price_period, period_rsi)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal_sma"] = np.where(
(price_signal_period[period_sma_str] <= price_signal_period["Close"]),
1, -1)
price_signal_period["Signal_rsi"] = np.where(
(price_signal_period["rsi"] > 90), -1,
np.where((price_signal_period["rsi"] < 10), 1, 0))
price_signal_period["Signal"] = price_signal_period.apply(
lambda x: signal_generation(*x), axis=1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Signal"] = price_signal[["Close", "Signal"]].apply(
lambda x: stop_loss(*x, stop_level), axis=1)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def breakout_system(price_data,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Buy Level"] = price_signal_period["Open"] + (
price_signal_period["Close"].shift(1) -
price_signal_period["Low"].shift(1))
price_signal_period["Sell Level"] = price_signal_period["Open"] - (
price_signal_period["High"].shift(1) -
price_signal_period["Close"].shift(1))
price_signal_period["Position window today"] = np.where(
price_signal_period["Open"] > price_signal_period["Close"].shift(1), 1,
np.where(
price_signal_period["Open"] <
price_signal_period["Close"].shift(1), -1, 0))
price_signal_period["Position window yesterday"] = price_signal_period[
"Position window today"].shift(1)
Signal = price_signal_period.apply(lambda x: signal_generation(*x), axis=1)
Signal = Signal.apply(pd.Series)
Signal.columns = ["Signal", "Trade_Level"]
price_signal_period = pd.concat([price_signal_period, Signal], axis=1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trade_Level"] = price_signal_period["Trade_Level"]
price_signal["Trade_Level"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Trade_Level"],
underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Trade_Level")
return trades, price_signal
def seasonal_short_system(price_data,
entry_date=16,
exit_date=22,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period = ""
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = -1
if entry_date < exit_date:
price_signal_period["Signal"][
price_signal_period.index.day > exit_date] = 0
price_signal_period["Signal"][
price_signal_period.index.day <= entry_date] = 0
else:
price_signal_period["Signal"][
price_signal_period.index.day > exit_date] = 0
price_signal_period["Signal"][
price_signal_period.index.day > entry_date] = -1
price_signal_period.loc[price_signal_period["Signal"].shift(-1) == -1,
"Signal"] = -1
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"] = price_signal["Signal"].shift(1)
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal["Trades"].fillna(0, inplace=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Close"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Close")
return trades, price_signal
def expanding_bollinger_band_system(price_data,
period1=10,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period_sma = str(period1) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
sma.sma(price_period, period1)
price_signal_period = price_period
price_signal = price_data
price_signal_period["std_level"] = price_signal_period["Close"].rolling(
period1).std()
price_signal_period["std_sma"] = price_signal_period["std_level"].rolling(
period1 * 2).mean()
price_signal_period["expanding_bollinger"] = np.where(
(price_signal_period["std_level"] > price_signal_period["std_sma"]), 1,
0)
price_signal_period["sma_signal"] = np.where(
(price_signal_period["Close"] > price_signal_period[period_sma]), 1,
-1)
price_signal_period["Signal"] = price_signal_period[
"expanding_bollinger"] * price_signal_period["sma_signal"]
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def seasonal_short_sma_system(price_data,
period_sma=10,
entry_date=16,
exit_date=22,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period = ""
period_str = str(period_sma) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
sma.sma(price_signal_period, period_sma)
price_signal_period["sma_signal"] = np.where(
price_signal_period["Close"] < price_signal_period[period_str], -1, 0)
price_signal_period["Signal"] = np.nan
price_signal_period["Signal"] = signal_generation(price_signal_period,
entry_date, exit_date)
price_signal_period["Signal"].fillna(method="ffill", inplace=True)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal["Trades"].fillna(0, inplace=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Close"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Close")
return trades, price_signal
def rsi50_55sma_system(price_data,
period_sma=55,
period_rsi=14,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period_sma_str = str(period_sma) + "_SMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
sma.sma(price_period, period_sma)
rsi.rsi(price_period, period_rsi)
price_signal_period = price_period
price_signal = price_data
# price_signal_period[period_sma] = sma_1
price_signal_period["Signal"] = np.where(
(price_signal_period[period_sma_str] <= price_signal_period["Close"]) &
(price_signal_period["rsi"] > 50), 1,
np.where(
(price_signal_period[period_sma_str] >
price_signal_period["Close"]) & (price_signal_period["rsi"] < 50),
-1, 0))
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def roc_system(price_data,
period1=10,
period2=25,
period3=40,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal = price_data
price_signal_period = price_period
roc.roc(price_period, period1)
roc.roc(price_period, period2)
roc.roc(price_period, period3)
price_signal_period["roc_average"] = price_period[[
str(period1) + "_roc",
str(period2) + "_roc",
str(period3) + "_roc"
]].mean(axis=1)
price_signal_period["Signal"] = np.where(
(price_signal_period["roc_average"] > 0), 1, -1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal["Trades"].fillna(0, inplace=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, None, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def rsi2_system(price_data,
period_rsi=2,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
rsi.rsi(price_period, period_rsi)
price_signal_period = price_period
price_signal = price_data
price_signal_period["rsi_1"] = price_signal_period["rsi"].shift(1)
price_signal_period["rsi_1"].fillna(0, inplace=True)
price_signal_period["Signal"] = np.where(
((price_signal_period["rsi"] > 10) &
(price_signal_period["rsi_1"] < 10)), 1,
np.where(((price_signal_period["rsi"] < 90) &
(price_signal_period["rsi_1"] > 90)), -1, 0))
price_signal_period["Signal"].replace(to_replace=0,
method="ffill",
inplace=True)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def kchannel_system(price_data,
period1=20,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
period_ema = str(period1) + "_EMA"
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
ema.ema(price_period, period1)
atr.atr(price_period, period1)
price_period["Upper band"] = price_period[period_ema] + price_period["ATR"]
price_period["Lower band"] = price_period[period_ema] - price_period["ATR"]
price_signal_period = price_period
price_signal = price_data
price_signal_period["Signal"] = np.where(
(price_signal_period["Close"] > price_signal_period["Upper band"]), 1,
np.where(
(price_signal_period["Close"] < price_signal_period["Lower band"]),
-1, np.nan))
price_signal_period["Signal"].fillna(method="ffill", inplace=True)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def assymetric_system(price_data,
period_1=10,
period_2=20,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
sma.sma(price_period, period_1)
sma.sma(price_period, period_2)
price_signal_period = price_period
price_signal_period["min_of_2"] = price_signal_period[[
str(period_1) + "_SMA", str(period_2) + "_SMA"
]].min(axis=1)
price_signal = price_data
price_signal_period["Signal"] = np.where(
(price_signal_period["Close"] > price_signal_period["min_of_2"]), 1,
-1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
price_signal.to_csv("Price_signal.csv", index=True)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def stochastics_system(price_data, period1=14,period2=3,long_level=50,short_level=50, long_stop=0,short_stop=0, period="", trade_type="Both_leg", underlying_instrument_data=None):
if long_stop==0:
long_stop=short_level
if short_stop==0:
short_stop=long_level
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(price_data, period)
highest_high=price_period["High"].rolling(period1).max()
lowest_low=price_period["Low"].rolling(period1).min()
percentage_k=((price_period["Close"]-lowest_low)/(highest_high-lowest_low))*100
price_signal_period = price_period
price_signal_period["percentage_d"] = percentage_k.rolling(period2).mean()
price_signal = price_data
price_signal_period["Signal"] = np.where(((price_signal_period["percentage_d"] > long_level) & (price_signal_period["percentage_d"].shift() < long_level)), 1,
np.where(((price_signal_period["percentage_d"] < short_level) & (price_signal_period["percentage_d"].shift() > short_level)), -1,
np.where(((price_signal_period["percentage_d"] < long_stop) & (price_signal_period["percentage_d"].shift() > long_stop)),0,
np.where(((price_signal_period["percentage_d"] > short_stop) & (price_signal_period["percentage_d"].shift() < short_stop)),0,
np.nan))))
price_signal_period["Signal"].fillna(method="ffill",inplace=True)
price_signal["Signal"] = price_signal_period["Signal"].resample("D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(price_signal, underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def ma_trailing_stop_system(price_data,
std_period=20,
trailing_stop=3,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
trailing_stop = trailing_stop / 100
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["std_level"] = (
price_signal_period["Close"] -
price_signal_period["Close"].rolling(std_period).mean()
) / price_signal_period["Close"].rolling(std_period).std()
price_signal_period["Signal"] = 0
signal_generation(price_signal_period, trailing_stop)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Close"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def island_reversal_system(price_data,
profit_target=10,
stop_level=2,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
profit_target = profit_target / 100
stop_level = -stop_level / 100
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
island_reversal.island_reversal(price_signal_period)
price_signal_period["Signal"] = price_signal_period.apply(
lambda x: signal_generation(*x, profit_target, stop_level), axis=1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Close"], underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal)
return trades, price_signal
def keltner_system(price_data,
period1=21,
period2=21,
period="",
trade_type="Both_leg",
underlying_instrument_data=None):
if period == "":
price_period = price_data
else:
price_period = series_resampling.price_series_periodic(
price_data, period)
price_signal_period = price_period
price_signal = price_data
price_signal_period["Average_close"] = price_signal_period[[
"High", "Low", "Close"
]].mean(axis=1)
price_signal_period["Average_close_average"] = price_signal_period[
"Average_close"].rolling(period1).mean()
true_range = pd.concat([
price_signal_period["High"] - price_signal_period["Low"],
abs(price_signal_period["High"] -
price_signal_period["Close"].shift()),
abs(price_signal_period["Low"] - price_signal_period["Close"].shift())
],
axis=1)
true_range = true_range.max(axis=1)
price_signal_period["atr"] = true_range.rolling(period2).mean()
price_signal_period["upper_band"] = price_signal_period[
"Average_close_average"] + 1.3 * price_signal_period["atr"]
price_signal_period["lower_band"] = price_signal_period[
"Average_close_average"] - 1.3 * price_signal_period["atr"]
cols = ["Average_close_average", "upper_band", "lower_band"]
price_signal_period[cols] = price_signal_period[cols].shift(fill_value=0)
price_signal_period["Average_close_signal"] = np.where(
price_signal_period["Average_close_average"] >=
price_signal_period["Average_close_average"].shift(), 1, -1)
Signal = price_signal_period.apply(lambda x: signal_generation(*x), axis=1)
Signal = Signal.apply(pd.Series)
Signal.columns = ["Signal", "Trade_Level"]
price_signal_period = pd.concat([price_signal_period, Signal], axis=1)
price_signal["Signal"] = price_signal_period["Signal"].resample(
"D").ffill()
price_signal["Signal"].fillna(0, inplace=True)
price_signal["Trade_Level"] = price_signal_period["Trade_Level"]
price_signal["Trade_Level"].fillna(0, inplace=True)
price_signal[
"Trades"] = price_signal["Signal"] - price_signal["Signal"].shift(1)
if trade_type == "Individual":
trades = creating_individual_trade.creating_individual_trade(
price_signal, price_signal["Trade_Level"],
underlying_instrument_data)
else:
trades = tc.trade_generation(price_signal, "Trade_Level")
return trades, price_signal
