def fullGapPositive(df):
diff=df['open']>df['high'].shift(1)
df= df[diff]
df['sign']=1
df['typeid']=alert_constants.Full_Gap_Up
dbdao.savealerts(df)
def volumeNegative(df):
diff=( (df['volume']<0.65*df['sma_volume_6month']) )
df= df[diff]
df['sign']=-1
df['typeid']=alert_constants.Negative_Volume
dbdao.savealerts(df)
def keyReversalNegative(df):
diff=((df['open']>df['low'].shift(1)) & (df['close']<df['close'].shift(1)) & (df['close']<df['high'].shift(1)) &(df['volume']>df['sma_volume_6month']))
df= df[diff]
df['sign']=-1
df['typeid']=alert_constants.Negative_Key_Reversal
dbdao.savealerts(df)
def volumePositive(df):
diff=( (df['volume']>=1.45*df['sma_volume_6month']) )
df= df[diff]
df['sign']=1
df['typeid']=alert_constants.Positive_Volume
dbdao.savealerts(df)
def partialGapPositive(df):
diff=df['open']>df['close'].shift(1)
df= df[diff]
df['sign']=1
df['typeid']=alert_constants.Partial_Gap_up
dbdao.savealerts(df)
def partialGapNegative(df):
diff=df['open']<df['close'].shift(1)
df= df[diff]
df['sign']=-1
df['typeid']=alert_constants.Partial_Gap_Down
dbdao.savealerts(df)
def obos_alerts(df):
df['rsi_value'] = df['rsi'].apply(rsi_manager.calculate_rsi_values)
df_aos = df.loc[df['rsi_value'] == constants.RSI_ApproachingOversold]
df_aos['sign'] = 1
df_aos['typeid'] = alert_constants.ApproachingOversold
df_aos['text'] = 'ApproachingOversold'
df_os = df.loc[(df['rsi_value'] == constants.RSI_Oversold)]
df_os['sign'] = 1
df_os['typeid'] = alert_constants.Oversold
df_os['text'] = 'Oversold'
df_aob = df.loc[(df['rsi_value'] == constants.RSI_ApproachingOverbought)]
df_aob['sign'] = -1
df_aob['typeid'] = alert_constants.ApproachingOverbought
df_aob['text'] = 'ApproachingOverbought'
df_ob = df.loc[(df['rsi_value'] == constants.RSI_Overbought)]
df_ob['sign'] = -1
df_ob['typeid'] = alert_constants.Overbought
df_ob['text'] = 'Overbought'
df_merged = pd.concat([df_aob, df_aos, df_os, df_ob], axis=0)
dbdao.savealerts(df_merged)
def fullGapNegative(df):
diff=df['open']<df['low'].shift(1)
df= df[diff]
df['sign']=-1
df['typeid']=alert_constants.Full_Gap_Down
dbdao.savealerts(df)
def obos_alerts(df):
df['rsi_value'] = df['rsi'].apply(rsi_manager.calculate_rsi_values )
df_aos = df.loc[df['rsi_value'] ==constants.RSI_ApproachingOversold]
df_aos['sign']=1
df_aos['typeid']=alert_constants.ApproachingOversold
df_aos['text']='ApproachingOversold'
df_os = df.loc[(df['rsi_value'] ==constants.RSI_Oversold)]
df_os['sign']=1
df_os['typeid']=alert_constants.Oversold
df_os['text']='Oversold'
df_aob= df.loc[(df['rsi_value'] ==constants.RSI_ApproachingOverbought)]
df_aob['sign']=-1
df_aob['typeid']=alert_constants.ApproachingOverbought
df_aob['text']='ApproachingOverbought'
df_ob = df.loc[(df['rsi_value'] ==constants.RSI_Overbought)]
df_ob['sign']=-1
df_ob['typeid']=alert_constants.Overbought
df_ob['text']='Overbought'
df_merged = pd.concat([df_aob,df_aos,df_os,df_ob],axis=0)
dbdao.savealerts(df_merged)
def TrendChangeNegative(df, column1, typeid):
crossing = ((df[column1] < df[column1].shift(1)))
crossing_dates = df.loc[crossing]
crossing_dates['sign'] = -1
crossing_dates['typeid'] = typeid
df_alerts = crossing_dates.rename(columns={column1: "newvalue"})
dbdao.savealerts(df_alerts)
def macd_crossovers(df):
df_bull_signal= bullish_co(df, 'macdhist',alert_constants.MACD_ABOVE_SIGNAL,'MACD crosses above signal line')
df_bear_signal=bearish_co(df, 'macdhist',alert_constants.MACD_BELOW_SIGNAL,'MACD crosses below signal line')
df_bull_center= bullish_co(df, 'macd',alert_constants.MACD_ABOVE_CENTER,'MACD crosses above center line')
df_bear_center=bearish_co(df, 'macd',alert_constants.MACD_BELOW_CENTER,'MACD crosses below center line')
df_merged=pd.concat([df_bull_signal,df_bear_signal,df_bull_center,df_bear_center],axis=0)
dbdao.savealerts(df_merged)
def TrendChangeNegative(df,column1,typeid):
crossing = ((df[column1] <df[column1].shift(1) ))
crossing_dates = df.loc[crossing]
crossing_dates['sign']=-1
crossing_dates['typeid']=typeid
df_alerts=crossing_dates.rename(columns={column1: "newvalue"})
dbdao.savealerts(df_alerts)
def relative_strength(df_merged):
df_alert= crossover_manager.bullish_co(df_merged,'Relative_strength',alert_constants.Relative_strength,"")
dbdao.savealerts(df_alert)
#print df_merged
# df_merged["rs"]=df_merged.apply(calculateRelativeStrength,axis=0)
# print df_merged
#df.apply(lambda x: x- x.shift(120))
def give_negative_co_dates(df,column1,column2,typeid,text):
previous_col1 = df[column1].shift(1)
previous_col2 = df[column2].shift(1)
crossing = ((df[column1] <= df[column2]) & (previous_col1 >= previous_col2))
crossing_dates = df.loc[crossing]
crossing_dates['sign']=-1
crossing_dates['typeid']=typeid
crossing_dates['text']=text
dbdao.savealerts(crossing_dates)
def give_negative_co_dates(df, column1, column2, typeid, text):
previous_col1 = df[column1].shift(1)
previous_col2 = df[column2].shift(1)
crossing = ((df[column1] <= df[column2]) &
(previous_col1 >= previous_col2))
crossing_dates = df.loc[crossing]
crossing_dates['sign'] = -1
crossing_dates['typeid'] = typeid
crossing_dates['text'] = text
dbdao.savealerts(crossing_dates)
def macd_crossovers(df):
df_bull_signal = bullish_co(df, 'macdhist',
alert_constants.MACD_ABOVE_SIGNAL,
'MACD crosses above signal line')
df_bear_signal = bearish_co(df, 'macdhist',
alert_constants.MACD_BELOW_SIGNAL,
'MACD crosses below signal line')
df_bull_center = bullish_co(df, 'macd', alert_constants.MACD_ABOVE_CENTER,
'MACD crosses above center line')
df_bear_center = bearish_co(df, 'macd', alert_constants.MACD_BELOW_CENTER,
'MACD crosses below center line')
df_merged = pd.concat(
[df_bull_signal, df_bear_signal, df_bull_center, df_bear_center],
axis=0)
dbdao.savealerts(df_merged)
