def adding_indicators(df, indicators: list = []):
for indicator in indicators:
if indicator[0] not in df.columns:
Indicators.AddIndicator(df=df,
indicator_name=indicator[0],
col_name=indicator[0],
args=indicator[1])
def maCrossoverStrategy(df, i: int):
""" If price is 10% below the Slow MA, return True """
if not df.__contains__('50_ema') and not df.__contains__('200_ema'):
Indicators.AddIndicator(df,
indicator_name="ema",
col_name="50_ema",
args=50)
Indicators.AddIndicator(df,
indicator_name="ema",
col_name="200_ema",
args=200)
if i > 0 and df['50_ema'][i-1] <= df['200_ema'][i-1] and \
df['50_ema'][i] > df['200_ema'][i]:
return df['close'][i]
return False
def maStrategy(df, i:int):
''' If price is 10% below the Slow MA, return True'''
if not df.__contains__('slow_sma'):
Indicators.AddIndicator(df, indicator_name="sma", col_name="slow_sma", args=30)
buy_price = 0.96 * df['slow_sma'][i]
if buy_price >= df['close'][i]:
return min(buy_price, df['high'][i])
return False
def bollStrategy(df, i:int):
''' If price is 2.5% below the Lower Bollinger Band, return True'''
if not df.__contains__('low_boll'):
Indicators.AddIndicator(df, indicator_name="lbb", col_name="low_boll", args=14)
buy_price = 0.975 * df['low_boll'][i]
if buy_price >= df['close'][i]:
return min(buy_price, df['high'][i])
return False
def ichimokuBullish(df, i:int):
''' If price is above the Cloud formed by the Senkou Span A and B,
and it moves above Tenkansen (from below), that is a buy signal.'''
if not df.__contains__('tenkansen') or not df.__contains__('kijunsen') or \
not df.__contains__('senkou_a') or not df.__contains__('senkou_b'):
Indicators.AddIndicator(df, indicator_name="ichimoku", col_name=None, args=None)
if i - 1 > 0 and i < len(df):
if df['senkou_a'][i] is not None and df['senkou_b'][i] is not None:
if df['tenkansen'][i] is not None and df['tenkansen'][i-1] is not None:
if df['close'][i-1] < df['tenkansen'][i-1] and \
df['close'][i] > df['tenkansen'][i] and \
df['close'][i] > df['senkou_a'][i] and \
df['close'][i] > df['senkou_b'][i]:
return df['close'][i]
return False
