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看了聚寬上大神寫的上漲趨勢(shì)策略，感覺(jué)不錯(cuò)。感興趣的可以轉(zhuǎn)至苦咖啡《上漲趨勢(shì)策略修改版》，地址為：

https://www.joinquant.com/view/community/detail/15c783ea414ddac3abb8fd55f39df2cc?type=1

隨著中美對(duì)抗的持續(xù)進(jìn)行，原有很多以美國(guó)為出口主要對(duì)象的企業(yè)必然走向內(nèi)卷，大企業(yè)之間的競(jìng)爭(zhēng)也將進(jìn)一步加大，只會(huì)強(qiáng)者恒強(qiáng)，進(jìn)一步走向壟斷。A股全面注冊(cè)制也將到來(lái)，小盤股時(shí)代終將結(jié)束，跟著機(jī)構(gòu)抱團(tuán)，找大型的，具有壟斷特征的股票才是出路。

作者在掘金平臺(tái)上對(duì)該策略進(jìn)行了實(shí)現(xiàn)，對(duì)2019到2020年進(jìn)行了回測(cè)，表現(xiàn)不錯(cuò)。

策略源碼如下，覺(jué)得有幫助的，還望點(diǎn)贊關(guān)注！對(duì)量化投資感興趣的小伙伴可以添加如下微信群，一起用技術(shù)改變世界，實(shí)現(xiàn)財(cái)務(wù)自由。

掘金實(shí)盤可參考掘金量化實(shí)盤指導(dǎo)

# coding=utf-8from gm.api import *
# 克隆自聚寬文章：https://www.joinquant.com/post/31232# 標(biāo)題：2020年 267%年化 13%回撤 上漲趨勢(shì)策略修改版# 作者：苦咖啡
# 克隆自聚寬文章：https://www.joinquant.com/post/31232# 標(biāo)題：2020年 267%年化 13%回撤 上漲趨勢(shì)策略修改版# 作者：苦咖啡
# 克隆自聚寬文章：https://www.joinquant.com/post/30863# 標(biāo)題：上漲股策略2020年收益123%# 作者：scottchenrui
#  2021-02-06#  張連重 掘金實(shí)現(xiàn)
import numpy as npfrom scipy.stats import linregressfrom gm.api import *from datetime import datetimeimport pandas as pdfrom jqdatasdk import *
#?jqdata?login??需替換為個(gè)人聚寬賬號(hào)auth('15520768082','ZXXXXXX')

# 初始化函數(shù)，設(shè)定要操作的股票、基準(zhǔn)等等def init(context):    # 最大建倉(cāng)數(shù)量    context.max_hold_stock_nums = 2    # 選出來(lái)的股票    context.target_lists = []
    schedule(schedule_func=check_stocks, date_rule='1w', time_rule='09:32:00')    schedule(schedule_func=sell, date_rule='1w', time_rule='09:34:00')    schedule(schedule_func=buy, date_rule='1w', time_rule='09:35:00')

# 股票篩選def check_stocks(context):    today = context.now.strftime("%Y-%m-%d")    last_date = get_trade_days(end_date=today, count=2)[0]    current = context.now.strftime("%Y-%m-%d %H:%M:%S")    last_date = get_trade_days(end_date=today, count=2)[0]    # 滬深300成分股    check_out_lists = get_index_stocks("000300.XSHG", date=last_date)
    check_out_lists = st_check(context,check_out_lists)    check_out_lists = science_check(context,check_out_lists)    stocks = filter_stks_listed_n_days(check_out_lists,last_date,100)    df = get_price(stocks,end_date=current,frequency='1m',fields=['open','close','high_limit','low_limit','paused'] ,count=1,panel=False)    df = df[(df['close'] < df['high_limit']) & (df['close'] > df['low_limit']) & (df['paused'] == 0)]    checked_stocks = df['code'].tolist()
    # 昨收盤價(jià)不高于260元/股    s_close_1 = get_price(checked_stocks,end_date=last_date,frequency='1d',fields=['close'] ,count=1,panel=False)    s_close_1.drop(['time'], axis=1,inplace=True)    s_close_1.set_index(["code"], inplace=True)    check_out_lists = list(s_close_1[s_close_1['close'] <= 260].index)
    # 近30個(gè)交易日的最高價(jià) / 昨收盤價(jià) <=1.1, 即HHV(HIGH,30)/C[-1] <= 1.1    high_30 = get_price(check_out_lists,end_date=last_date,frequency='1d',fields=['high'] ,count=30,panel=False)    high_max_30 = high_30.pivot(index='time',columns='code',values='high').max()    dict_high_max_30 = {'code':high_max_30.index,'high':high_max_30.values}    df_high_max_30 = pd.DataFrame(dict_high_max_30)    df_high_max_30.set_index('code',drop=True,inplace=True)    s_fall = df_high_max_30['high'] / s_close_1['close']    check_out_lists = list(s_fall[s_fall <= 1.1].index)    # 近7個(gè)交易日的交易量均值 與 近180給交易日的成交量均值 相比，放大不超過(guò)1.5倍  MA(VOL,7)/MA(VOL,180) <=1.5    volume_180 = get_price(check_out_lists,end_date=last_date,frequency='1d',fields=['volume'] ,count=180,panel=False)    volume_mean_7 = volume_180.pivot(index='time',columns='code',values='volume').iloc[-7:].mean()    volume_mean_180 = volume_180.pivot(index='time',columns='code',values='volume').mean()    s_vol_ratio = volume_mean_7 / volume_mean_180    check_out_lists = list(s_vol_ratio[s_vol_ratio <= 1.5].index)

    # 對(duì)近120個(gè)交易日的股價(jià)進(jìn)行線性回歸：入選條件 slope / intercept > 0.005 and r_value**2 > 0.8    target_dict = {}    x = np.arange(120)    for stock in check_out_lists:        y = get_price(stock,end_date=last_date,frequency='1d',fields=['close'] ,count=120,panel=False)['close']        slope, intercept, r_value, p_value, std_err = linregress(x, y)        if slope / intercept > 0.005 and r_value ** 2 > 0.8:            target_dict[stock] = r_value ** 2
    # 入選股票按照R Square 降序排序, 取前N名    context.target_lists = []    if target_dict:        df_score = pd.DataFrame.from_dict(            target_dict, orient='index', columns=['score', ]        ).sort_values(            by='score', ascending=False        )        #        context.target_lists = list(df_score.index[:context.max_hold_stock_nums])
# 去除ST股票def st_check(context,security_list):    currentDate = context.now.strftime("%Y-%m-%d")    current_data = get_extras('is_st', security_list, end_date=currentDate, df=True, count=1)       security_list = [stock for stock in security_list if not current_data[stock][-1]]    # 返回結(jié)果    return security_list
#def filter_stks_listed_n_days(source_stk_list, end_date, n=100):    # type: (list, Union[str, datetime.date, datetime.datetime], int) -> list    """    過(guò)濾掉source_stk_list中尚未上市的，或者上市天數(shù)不足n天的股票    """    # 1. 取得n個(gè)交易日之前的交易日期trd_date    trd_date = get_trade_days(end_date=end_date, count=n)[0]    # 2. 取trd_date日就已經(jīng)上市的所有股票    all_stks = get_all_securities(date=trd_date)    # 3. 過(guò)濾source_stk_list，剔除掉不在all_stks中的    valid_stk_list = list(all_stks[all_stks.index.isin(source_stk_list)].index)    return valid_stk_list
# 去除科創(chuàng)板股票def science_check(context,security_list):    stock_list = []    for stock in security_list:         if not stock.startswith('688'):            stock_list.append(stock)     # 返回結(jié)果    return stock_list



# 交易函數(shù) - 入場(chǎng)def buy(context):    buy_lists = context.target_lists    if buy_lists:        amount = context.account().cash['nav'] / len(buy_lists)        for stock in buy_lists:            stock_code_jj = transStockCode(stock,'JJ')            if stock_code_jj in get_position_list(context):                account_position = context.account().position(symbol=stock_code_jj,side = PositionSide_Long)                if abs(amount - account_position['amount']) > account_position['price'] * 100 + 5:                    _order = order_target_value(symbol=stock, value=amount, position_side=PositionSide_Long, order_type=OrderType_Market)                    if _order is not None:                        print('調(diào)倉(cāng): %s (%s)' % (get_security_info(stock).display_name, stock))        for stock in buy_lists:            stock_code_jj = transStockCode(stock,'JJ')            if stock_code_jj not in get_position_list(context):                _order = order_target_value(symbol=stock_code_jj, value=amount, position_side=PositionSide_Long, order_type=OrderType_Market)                if _order is not None:                   print('買入: %s (%s)' % (get_security_info(stock).display_name, stock))

def sell(context):    """    賣出不在buy_lists中的股票    """    buy_lists = context.target_lists    for stock in get_position_list(context):        stock_code_jk = transStockCode(stock,'JK')        if stock_code_jk not in buy_lists:            _order = order_target_value(symbol=stock, value=0, position_side=PositionSide_Long, order_type=OrderType_Market)            if _order is not None:                print('賣出: %s (%s)' % (get_security_info(stock_code_jk).display_name, stock))
 ##     def transStockListCode(stock_list_src,target_type):     stock_list_target = []     if target_type=='JJ':         for stock in stock_list_src:             target = 'SHSE' if stock[7:]=='XSHG' else 'SZSE'             stock_list_target.append(target +'.'+ stock[0:6])     elif  target_type=='JK':        for stock in stock_list_src:             target = 'XSHG' if stock[0:4]=='SHSE' else 'XSHE'             stock_list_target.append(stock[5:] +'.'+ target)        return stock_list_target        

## 單股名稱轉(zhuǎn)換def transStockCode(stock_src,target_type):     if target_type=='JJ':        target = 'SHSE' if stock_src[7:]=='XSHG' else 'SZSE'        return target +'.'+ stock_src[0:6]     elif  target_type=='JK':        target = 'XSHG' if stock_src[0:4]=='SHSE' else 'XSHE'        return stock_src[5:] +'.'+ target   
## 獲取持倉(cāng)股票列表def get_position_list(context):    in_stock = []    Account_positions = context.account().positions()    for position in Account_positions:        in_stock.append(position['symbol'])    return in_stock 
if __name__ == '__main__':    '''        strategy_id策略ID, 由系統(tǒng)生成        filename文件名, 請(qǐng)與本文件名保持一致        mode運(yùn)行模式, 實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST        token綁定計(jì)算機(jī)的ID, 可在系統(tǒng)設(shè)置-密鑰管理中生成        backtest_start_time回測(cè)開始時(shí)間        backtest_end_time回測(cè)結(jié)束時(shí)間        backtest_adjust股票復(fù)權(quán)方式, 不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST        backtest_initial_cash回測(cè)初始資金        backtest_commission_ratio回測(cè)傭金比例        backtest_slippage_ratio回測(cè)滑點(diǎn)比例        '''    run(strategy_id='b056605f-687a-11eb-8a4e-5254009e6375',        filename='main.py',        mode=MODE_BACKTEST,????????token='f8e34a0dacdbbbba2f2cab37b9c01772f80c53',        backtest_start_time='2019-01-01 08:00:00',        backtest_end_time='2021-02-10 16:00:00',        backtest_adjust=ADJUST_PREV,        backtest_initial_cash=50000,        backtest_commission_ratio=0.0001,        backtest_slippage_ratio=0.0001)