交易记录=追涨杀跌.追涨杀跌交易策略(股票代码,df.Low,df.High,df.Close,int(参数[0][0:2]),int(参数[1][0:2]))交易记录.计算收益()if(策略=="布林线策略"):参数=int(参数[0][0:2])ma20=布林线策略.ma(df.Close,参数)up_bb, low_bb=布林线策略.bull(df.Close,ma20,参数)交易记录=布林线...
apple_price_history[['open', 'high', 'low', 'close', 'volume']].head() 让我们查看数据框的数据类型或 dtypes,看看是否有任何日期时间信息。 让我们将数据框的 RangeIndex 更改为 DatetimeIndex。为了好看,我们将展示如何使用 read_csv 用 DatetimeIndex 读取数据。 python apptime64) apple_price_history....
我们首先将其转换为日期时间格式9stock_data['trade_date'] = pd.to_datetime(stock_data['trade_date'])1011#然后将'date'列设置为索引12stock_data.set_index('trade_date', inplace=True)1314#确保'volume'列的名字正确15stock_data.rename(columns=...
pfast=10,# 短期移动平均线周期 pslow=30# 长期移动平均线周期 ) def__init__(self): sma1 = bt.ind.SMA(period=self.p.pfast)# 短期移动平均线 sma2 = bt.ind.SMA(period=self.p.pslow)# 长期移动平均线 self.crossover= bt.ind.CrossOver(sma1, sma2)# 移动平均线交叉信号 defnext(self): ...
apple_price_history[['open', 'high', 'low', 'close', 'volume']].head() 1. 2. 3. 让我们查看数据框的数据类型或 dtypes,看看是否有任何日期时间信息。 让我们将数据框的 RangeIndex 更改为 DatetimeIndex。为了好看,我们将展示如何使用read_csv用 DatetimeIndex 读取数据。
Tenkan-sen (Conversion Line): (9-period high +9-period low)/2)) The default settingis9periodsandcan be adjusted. On a daily chart, this lineisthe midpoint of the9-day high-lowrange, whichisalmost two weeks. Kijun-sen (Base Line): (26-period high +26-period low)/2)) ...
"unix_docker_scan_scope":"host","sonicos_offline_configs":"","netapp_offline_configs":"","junos_offline_configs":"","huawei_offline_configs":"","procurve_offline_configs":"","procurve_config_to_audit":"Saved/(show config)","fortios_offline_configs":"","fireeye_offline_configs":"","...
To enrich the resulted document representation, a new modified weighting function is proposed for weighting concepts based on statistics extracted from word embedding information. The generated vectors are characterized by interpretability, low dimensionality, high accuracy, and low computational costs when ...
{'Name': ['item ' + str(i) for i in list(range(1, 51)) ],'Value': np.random.randint(low=10, high=100, size=50) })# 排序df = df.sort_values(by=['Value'])# 初始化画布plt.figure(figsize=(20, 10))ax = plt.subplot(111, polar=True)plt.axis('off')...
column_names = ['datetime', 'open', 'close', 'high', 'low', 'volume', 'amount', 'amplitude', 'change_percent', 'change_amount', 'turnover_ratio'] df = pd.DataFrame(data, columns=column_names, dtype=float) return df # 获取简单移动平均线,参数有2个,一个是数据源,一个是日期 ...