https://bbs.quantclass.cn/thread/12369

def calculate(df, parameters, **kwargs):
    """
    计算单个币种的因子
    这边坚决不能进行去重或者删除空值等删除某行的操作
    :param df:  需要计算的币种数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    N1,N2=parameters
    DMA=df['close'].rolling(window=N1).mean()-df['close'].rolling(window=N2).mean()
    df['{}_{}'.format(factor_name, parameters)]=DMA
    df['{}MA_{}'.format(factor_name,parameters)]=DMA.rolling(window=N1).mean()
    return df

def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 9, 14, 23, 36, 57, 93]
    while len(listp)<20:
        _par=int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list

当DMA值上穿其移动平均线时买入

def event_strategy(df, params, **kwargs):
    """
    计算事件
    :param df: 数据
    :param params:
    :return:
    """
    event_name = '{}_{}'.format(strategy_name,params)  # 策略名称
    #-----long-------------
    #条件1，DMA上穿其均线，买入
    con1=df['DMA_{}'.format(params)]>df['DMAMA_{}'.format(params)]

    #条件2，上一周期，DMA在均线下
    #短线对应SMI
    df['DMA_shift_{}'.format(params)] = df.groupby('symbol')['DMA_{}'.format(params)].shift()
    #长线对应SMIMA
    df['DMAMA_shift_{}'.format(params)] = df.groupby('symbol')['DMAMA_{}'.format(params)].shift()
    con2=df['DMA_shift_{}'.format(params)] < df['DMAMA_shift_{}'.format(params)]
    df.loc[con1 & con2, event_name] = 1

    return df

import pandas as pd
factor_name = 'DMA'


def special_data(candle_df, symbol, agg_dict, **kwargs):
    """
    导入额外的数据
    :param candle_df:   基础数据
    :param symbol:  币种名
    :param agg_dict:   转换周期的字典
    :return:
    """

    return candle_df, agg_dict


def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 8, 13, 21, 34, 55, 89]
    while len(listp)<20:
        _par=int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list





def calculate(df, parameters, **kwargs):
    """
    计算单个币种的因子
    这边坚决不能进行去重或者删除空值等删除某行的操作
    :param df:  需要计算的币种数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    N1,N2=parameters
    DMA=df['close'].rolling(window=N1).mean()-df['close'].rolling(window=N2).mean()
    df['{}_{}'.format(factor_name, parameters)]=DMA
    df['{}MA_{}'.format(factor_name,parameters)]=DMA.rolling(window=N1).mean()
    return df


def cross_section_calculate(df, parameters, **kwargs):
    """
    计算截面因子的函数
    :param df:  所有币种的数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    df['{}_{}_排名' .format (factor_name, parameters)] = df.groupby('candle_begin_time')['{}_{}'.format(factor_name,parameters)].rank(ascending=False, method='first')
    return df

# 事件需要的因子
factors = ['DMA']
# 事件的名称名
strategy_name = 'DMA&event'  # 名称和程序文件名相同，不能含有_



def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 8, 13, 21, 34, 55, 89]
    while len(listp)<20:
        _par = int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list



def event_strategy(df, params, **kwargs):
    """
    计算事件
    :param df: 数据
    :param params:
    :return:
    """
    event_name = '{}_{}'.format(strategy_name,params)  # 策略名称
    #-----long-------------
    #条件1，DMA上穿其均线，买入
    con1=df['DMA_{}'.format(params)]>df['DMAMA_{}'.format(params)]

    #条件2，上一周期，DMA在均线下
    #短线对应SMI
    df['DMA_shift_{}'.format(params)] = df.groupby('symbol')['DMA_{}'.format(params)].shift()
    #长线对应SMIMA
    df['DMAMA_shift_{}'.format(params)] = df.groupby('symbol')['DMAMA_{}'.format(params)].shift()
    con2=df['DMA_shift_{}'.format(params)] < df['DMAMA_shift_{}'.format(params)]
    df.loc[con1 & con2, event_name] = 1

    return df

https://bbs.quantclass.cn/thread/12369

https://bbs.quantclass.cn/thread/12369

https://bbs.quantclass.cn/thread/12369

def calculate(df, parameters, **kwargs):
    """
    计算单个币种的因子
    这边坚决不能进行去重或者删除空值等删除某行的操作
    :param df:  需要计算的币种数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    N1,N2=parameters
    DMA=df['close'].rolling(window=N1).mean()-df['close'].rolling(window=N2).mean()
    df['{}_{}'.format(factor_name, parameters)]=DMA
    df['{}MA_{}'.format(factor_name,parameters)]=DMA.rolling(window=N1).mean()
    return df

def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 9, 14, 23, 36, 57, 93]
    while len(listp)<20:
        _par=int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list

当DMA值上穿其移动平均线时买入

def event_strategy(df, params, **kwargs):
    """
    计算事件
    :param df: 数据
    :param params:
    :return:
    """
    event_name = '{}_{}'.format(strategy_name,params)  # 策略名称
    #-----long-------------
    #条件1，DMA上穿其均线，买入
    con1=df['DMA_{}'.format(params)]>df['DMAMA_{}'.format(params)]

    #条件2，上一周期，DMA在均线下
    #短线对应SMI
    df['DMA_shift_{}'.format(params)] = df.groupby('symbol')['DMA_{}'.format(params)].shift()
    #长线对应SMIMA
    df['DMAMA_shift_{}'.format(params)] = df.groupby('symbol')['DMAMA_{}'.format(params)].shift()
    con2=df['DMA_shift_{}'.format(params)] < df['DMAMA_shift_{}'.format(params)]
    df.loc[con1 & con2, event_name] = 1

    return df

import pandas as pd
factor_name = 'DMA'


def special_data(candle_df, symbol, agg_dict, **kwargs):
    """
    导入额外的数据
    :param candle_df:   基础数据
    :param symbol:  币种名
    :param agg_dict:   转换周期的字典
    :return:
    """

    return candle_df, agg_dict


def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 8, 13, 21, 34, 55, 89]
    while len(listp)<20:
        _par=int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list





def calculate(df, parameters, **kwargs):
    """
    计算单个币种的因子
    这边坚决不能进行去重或者删除空值等删除某行的操作
    :param df:  需要计算的币种数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    N1,N2=parameters
    DMA=df['close'].rolling(window=N1).mean()-df['close'].rolling(window=N2).mean()
    df['{}_{}'.format(factor_name, parameters)]=DMA
    df['{}MA_{}'.format(factor_name,parameters)]=DMA.rolling(window=N1).mean()
    return df


def cross_section_calculate(df, parameters, **kwargs):
    """
    计算截面因子的函数
    :param df:  所有币种的数据
    :param parameters:  参数
    :param kwargs:    额外的参数
    :return:
    """
    df['{}_{}_排名' .format (factor_name, parameters)] = df.groupby('candle_begin_time')['{}_{}'.format(factor_name,parameters)].rank(ascending=False, method='first')
    return df

# 事件需要的因子
factors = ['DMA']
# 事件的名称名
strategy_name = 'DMA&event'  # 名称和程序文件名相同，不能含有_



def batch_parameters():
    """
    生成遍历的参数
    :return:
    """

    listp = [2, 3, 5, 8, 13, 21, 34, 55, 89]
    while len(listp)<20:
        _par = int(listp[-1]/0.8)
        listp.append(_par)
    parameters_list = []
    for short in listp:
        for long in listp:
            if short < long:
                parameters_list.append([short, long])
    return parameters_list



def event_strategy(df, params, **kwargs):
    """
    计算事件
    :param df: 数据
    :param params:
    :return:
    """
    event_name = '{}_{}'.format(strategy_name,params)  # 策略名称
    #-----long-------------
    #条件1，DMA上穿其均线，买入
    con1=df['DMA_{}'.format(params)]>df['DMAMA_{}'.format(params)]

    #条件2，上一周期，DMA在均线下
    #短线对应SMI
    df['DMA_shift_{}'.format(params)] = df.groupby('symbol')['DMA_{}'.format(params)].shift()
    #长线对应SMIMA
    df['DMAMA_shift_{}'.format(params)] = df.groupby('symbol')['DMAMA_{}'.format(params)].shift()
    con2=df['DMA_shift_{}'.format(params)] < df['DMAMA_shift_{}'.format(params)]
    df.loc[con1 & con2, event_name] = 1

    return df

https://bbs.quantclass.cn/thread/12369

https://bbs.quantclass.cn/thread/12369