xalpha package

xalpha.cons module

basic constants and functions

xalpha.cons.convert_date(date)

convert date into datetime object

Parameters:date – string of form ‘2017-01-01’ or datetime object
Returns:corresponding datetime object
xalpha.cons.myround(num, label=1)

correct implementation of round with round half up, round to 2 decimals

Parameters:
  • num – the floating number, to be rounded
  • label – integer 1 or 2, 1 for round half up while 2 for always round down
Returns:

the float number after rounding, with two decimals

xalpha.cons.today()
xalpha.cons.xirr(cashflows, guess=0.1)

calculate the Internal Rate of Return of a series of cashflows at irregular intervals.

Parameters:
  • cashflows – a list, in which each element is a tuple of the form (date, amount), where date is a datetime object and amount is an integer or floating number. Cash outflows (investments) are represented with negative amounts, and cash inflows (returns) are positive amounts.
  • guess – floating number, a guess at the xirr rate solution to be used as a starting point for the numerical solution
Returns:

the IRR as a single floating number

xalpha.cons.xnpv(rate, cashflows)

give the current cash value based on future cashflows

Parameters:
  • rate – float, the preset year rate
  • cashflows – a list, in which each element is a tuple of the form (date, amount), where date is a datetime object and amount is an integer or floating number. Cash outflows (investments) are represented with negative amounts, and cash inflows (returns) are positive amounts.
Returns:

a single float value which is the NPV of the given cash flows

xalpha.cons.yesterday()
xalpha.cons.yesterdaydash()
xalpha.cons.yesterdayobj()

xalpha.evaluate module

modules for evaluation and comparison on multiple object with price dataframe

class xalpha.evaluate.evaluate(*fundobjs, start=None)

Bases: object

多个 info 对象的比较类,比较的对象只要实现了 price 属性,该属性为具有 date 和 netvalue 列的 pandas.DataFrame 即可。 更进一步,也可讲做过 bcmkset 的 xalpha.multiple.mulfix 类作为输入,只不过此时需要提前额外指定以下该对象的 name 和 code 两个属性。 由于该类需要各基金净值表可以严格对齐,因此需要对节假日和国内不同的 QDII 基金进行补齐,由于第一个基金为基准,因此第一个输入不建议是 QDII 基金

Parameters:
  • fundobjs – info object,或者如前所述一切具有 price 表的对象
  • start – date string or object, 比较的起始时间,默认使用所有 price 表中最近的起始时间。 但需要注意,由于拉取的基金净值表,往往在开始几天缺失净值数据,即使使用默认时间也可能无法对齐所有净值数据。 因此建议手动设置起始时间到最近的起始时间一周后左右。
correlation_table(end=datetime.datetime(2018, 8, 19, 0, 0))

give the correlation coefficient amongst referenced funds and indexes

Parameters:end – string or object of date, the end date of the line
Returns:pandas DataFrame, with correlation coefficient as elements
v_correlation(end=datetime.datetime(2018, 8, 19, 0, 0), **vkwds)

各基金净值的相关程度热力图可视化

Parameters:end – string or object of date, the end date of the line
Returns:pyecharts.Heatmap object
v_netvalue(end=datetime.datetime(2018, 8, 19, 0, 0), **vkwds)

起点对齐归一的,各参考基金或指数的净值比较可视化

Parameters:
  • end – string or object of date, the end date of the line
  • vkwds – pyechart line.add() options
Returns:

pyecharts.Line object

xalpha.indicator module

module for implementation of indicator class, which is designed as MinIn for systems with netvalues

class xalpha.indicator.indicator

Bases: object

MixIn class provide quant indicator tool box which is desinged as interface for mulfix class as well as info class, who are both treated as a single fund with price table of net value. Most of the quant indexes, their name conventions, definitions and calculations are from joinquant. Make sure first run obj.bcmkset() before you want to use functions in this class.

algorithm_volatility(date=datetime.datetime(2018, 8, 19, 0, 0))
alpha(date=datetime.datetime(2018, 8, 19, 0, 0))
annualized_returns(start, date=datetime.datetime(2018, 8, 19, 0, 0))
Parameters:
  • price – price table of info().price
  • start – datetime obj for starting date of calculation
  • date – datetime obj for ending date of calculation
Returns:

float, annualized returns of the price table

bbi(col='netvalue')

多空指标 give bull and bear line in column BBI in price table

Parameters:col – string, column name in dataframe you want to calculate
bcmkset(infoobj, start=None, riskfree=0.0371724)

Once you want to utilize the indicator tool box for analysis, first run bcmkset function to set the benchmark, otherwise most of the functions would raise error.

Parameters:
  • infoobj – info obj, whose netvalue are used as benchmark
  • start – datetime obj, indicating the starting date of all analysis. Note if use default start, there may be problems for some fundinfo obj, as lots of funds lack netvalues of several days from our API, resulting unequal length between benchmarks and fund net values.
  • riskfree – float, annual rate in the unit of 100%, strongly suggest make this value consistent with the interest parameter when instanciate cashinfo() class
benchmark_annualized_returns(date=datetime.datetime(2018, 8, 19, 0, 0))
benchmark_volatility(date=datetime.datetime(2018, 8, 19, 0, 0))
beta(date=datetime.datetime(2018, 8, 19, 0, 0))
bias(window=10, col='netvalue')

乖离率 give the bias as BIAS column in price table

Parameters:
  • window – int, MA_window
  • col – string, column name in dataframe you want to calculate
boll(window=10, deviation=2, col='netvalue')

布林线上下轨计算 give the bolling upper and lower band in the price table, the middle line is just ma line

Parameters:
  • window – int, the date window for ma and md
  • deviation – int or float, how many times deviation of sigma
  • col – string, column name in dataframe you want to calculate
comparison(date=datetime.datetime(2018, 8, 19, 0, 0))
Returns:tuple of two pd.Dataframe, the first is for aim and the second if for the benchmark index all netvalues are normalized and set equal 1.00 on the self.start date
correlation_coefficient(date=datetime.datetime(2018, 8, 19, 0, 0))
correlation coefficient between aim and benchmark values,
可以很好地衡量指数基金的追踪效果
Returns:float between -1 and 1
dma(fast_window=10, slow_window=50, ama_window=10, col='netvalue')

平行线差指标 give different of moving average as columns DMA and AMA in price table

Parameters:
  • fast_window – int
  • slow_window – int
  • ama_window – int
  • col – string, column name in dataframe you want to calculate
ema(window=5, col='netvalue')

指数平均数指标 give the exponential moving average as a new column ‘EMA’ in the price table, return None

Parameters:
  • window – the span of date, where the decay factor alpha=2/(1+window)
  • col – string, column name in dataframe you want to calculate
information_ratio(date=datetime.datetime(2018, 8, 19, 0, 0))
kdj(rsv_window=9, k_window=3, d_window=3, col='netvalue')

KDJ 随机指标 由于该模块不涉及日内高低价的信息,因此区间最高价最低价都由极值收盘价代替,因此和其他软件计算的 kdj 指标可能存在出入。 give k,d,j indexes as three columns KDJ_K/D/J in price table

Parameters:
  • rsv_window – int
  • k_window – int
  • d_window – int
  • col – string, column name in dataframe you want to calculate
ma(window=5, col='netvalue')

移动平均线指标 give the moving average as a new column ‘MA’ in the price table, return None

Parameters:
  • window – the date window of the MA calculation
  • col – string, column name in dataframe you want to calculate
macd(fast_window=12, slow_window=26, signal_window=9, col='netvalue')

指数平滑异同移动平均线 give the MACD index as three new columns ‘MACD_DIFF/DEM/OSC’ in the price table, return None

Parameters:
  • fast_window – int,
  • slow_window – int,
  • signal_window – int, the ema window of the signal line
  • col – string, column name in dataframe you want to calculate
max_drawdown(date=datetime.datetime(2018, 8, 19, 0, 0))

回测时间段的最大回撤

Parameters:date – date obj or string
Returns:three elements tuple, the first two are the date obj of start and end of the time window, the third one is the drawdown amplitude in unit 1.
md(window=5, col='netvalue')

移动标准差指标 give the moving standard deviation as a new column ‘MD’ in the price table, return None

Parameters:
  • window – the date window of the MD calculation
  • col – string, column name in dataframe you want to calculate
mtm(window=10, col='netvalue')

动量指标,并未附加动量的平均线指标,如需计算动量平均线指标,使用ma或emca函数,col参数选择MTM列即可 give the MTM as a new column ‘MTM’ in the price table, return None

Parameters:
  • window – int, the difference between price now and window days ago
  • col – string, column name in dataframe you want to calculate
psy(count_window=12, ma_window=6, col='netvalue')

心理线指标(衡量过去 count_window 天涨幅天数) give psy and psyma as column PSY and PSYMA in price table

Parameters:
  • count_window – int
  • ma_window – int
  • col – string, column name in dataframe you want to calculate
ratedaily(date=datetime.datetime(2018, 8, 19, 0, 0))
roc(window=10, col='netvalue')

变动率指标 give the ROC as a new column ‘ROC’ in the price table, return None, the ROC is in the unit of 1 instead of 1%

Parameters:
  • window – int, the change rate between price now and window days ago
  • col – string, column name in dataframe you want to calculate
rsi(window=14, col='netvalue')

相对强弱指标 give the rsi as RSI column in price table

Parameters:
  • window – int, MA_window
  • col – string, column name in dataframe you want to calculate
sharpe(date=datetime.datetime(2018, 8, 19, 0, 0))
total_annualized_returns(date=datetime.datetime(2018, 8, 19, 0, 0))
total_return(date=datetime.datetime(2018, 8, 19, 0, 0))
trix(window=10, ma_window=10, col='netvalue')

三重指数平滑平均线 give the trix index in column TRIX, TRMA

Parameters:
  • window – int
  • col – string, column name in dataframe you want to calculate
v_netvalue(end=datetime.datetime(2018, 8, 19, 0, 0), benchmark=True, **vkwds)

visulaization on netvalue curve

Parameters:vkwds – parameters for the pyecharts options in line.add(), eg. yaxis_min=0.7
v_techindex(end=datetime.datetime(2018, 8, 19, 0, 0), col=None, **vkwds)

visualization on netvalue curve and specified indicators

Parameters:
  • end – date string or obj, the end date of the figure
  • col – list, list of strings for price col name, eg.[‘MA5’,’BBI’] remember generate these indicators before the visualization
  • vkwds – keywords option for pyecharts.Line().add(). eg, you may need is_symbol_show=False to hide the symbols on lines
volatility(date=datetime.datetime(2018, 8, 19, 0, 0))
wnr(window=14, col='netvalue')

威廉指标,这里取超卖结果接近0的约定(wnr*-1),事实上就是 rsv, 同样的区间极值价用极值收盘价替代 give williams %R in WNR column in price table

Parameters:
  • window – int
  • col – string, column name in dataframe you want to calculate

xalpha.info module

modules of info class, including cashinfo, indexinfo and fundinfo class

class xalpha.info.basicinfo(code)

Bases: xalpha.indicator.indicator

Base class for info of fund, index or even cash, which cannot be directly instantiate, the basic implementation consider redemption fee as zero when shuhui() function is implemented

Parameters:code – string of code for specific product
info()

print basic info on the class

shengou(value, date)

give the realdate deltacash deltashare tuple based on purchase date and purchase amount

Returns:three elements tuple, the first is the actual dateobj of commit the second is a negative float for cashin, the third is a positive float for share increase
shuhui(share, date, rem)

give the cashout considering redemption rates as zero

Returns:three elements tuple, the first is dateobj the second is a positive float for cashout, the third is a negative float for share decrease
class xalpha.info.cashinfo(interest=0.0001, start='2012-01-01')

Bases: xalpha.info.basicinfo

A virtual class for remaining cash manage: behave like monetary fund

Parameters:
  • interest – float, daily rate in the unit of 100%, note this is not a year return rate!
  • start – str of date or dateobj, the virtual starting date of the cash fund
class xalpha.info.fundinfo(code, label=1)

Bases: xalpha.info.basicinfo

class for specific fund with basic info and every day values 所获得的基金净值数据一般截止到昨日。但注意QDII基金的净值数据会截止的更早,因此部分时间默认昨日的函数可能出现问题, 处理QDII基金时,需要额外注意。

Parameters:
  • code – str, 基金六位代码字符
  • label – integer 1 or 2, 取2表示基金申购时份额直接舍掉小数点两位之后。当基金处于 cons.droplist 名单中时, label 总会被自动设置为2。非名单内基金可以显式令 label=2.
feedecision(day)

give the redemption rate in percent unit based on the days difference between purchase and redemption

Parameters:day – integer, 赎回与申购时间之差的自然日数
Returns:float,赎回费率,以%为单位
info()

print basic info on the class

shuhui(share, date, rem)

give the cashout based on rem term considering redemption rates

Returns:three elements tuple, the first is dateobj the second is a positive float for cashout, the third is a negative float for share decrease
class xalpha.info.indexinfo(code)

Bases: xalpha.info.basicinfo

Get everyday close price of specific index. In self.price table, totvalue column is the real index while netvalue comlumn is normalized to 1 for the start date. In principle, this class can also be used to save stock prices but the price is without adjusted.

Parameters:code – string with seven digitals! note the code here has an extra digit at the beginning, 0 for sh and 1 for sz.
class xalpha.info.mfundinfo(code)

Bases: xalpha.info.basicinfo

真实的货币基金类,可以通过货币基金六位代码,来获取真实的货币基金业绩,并进行交易回测等

Parameters:code – string of six digitals, code of real monetnary fund

xalpha.multiple module

module for mul and mulfix class: fund combination management

class xalpha.multiple.mul(*fundtradeobj, status=None)

Bases: object

multiple fund positions manage class

Parameters:
  • fundtradeobj – list of trade obj which you want to analyse together
  • status – the status table of trade, all code in this table would be considered one must provide one of the two paramters, if both are offered, status will be overlooked
combsummary(date=datetime.datetime(2018, 8, 19, 0, 0))

brief report table of every funds and the combination investment

Parameters:date – string or obj of date, show info of the date given
Returns:empty dict if nothing is remaining that date dict of various data on the trade positions
evaluation(start=None)

give the evaluation object to analysis funds properties themselves instead of trades

Returns:xalpha.evaluate.evaluate object, with referenced funds the same as funds we invested
tot(prop='基金现值', date=datetime.datetime(2018, 8, 19, 0, 0))

sum of all the values from one prop of fund daily report, of coures many of the props make no sense to sum

Parameters:prop – string defined in the daily report dict, typical one is ‘currentvalue’ or ‘originalpurchase’
v_positions(date=datetime.datetime(2018, 8, 19, 0, 0), **vkwds)

pie chart visulization of positions ratio in combination

v_positions_history(end='2018-08-19', **vkwds)

river chart visulization of positions ratio history use text size to avoid legend overlap in some sense, eg. legend_text_size=8

v_tradevolume(**vkwds)

visualization on trade summary of the funds combination

Parameters:vkwds – keyword argument for pyecharts Bar.add()
Returns:pyecharts.bar
xirrrate(date=datetime.datetime(2018, 8, 19, 0, 0), guess=0.1)

xirr rate evauation of the whole invest combination

class xalpha.multiple.mulfix(*fundtradeobj, status=None, totmoney=100000, cashobj=None)

Bases: xalpha.multiple.mul, xalpha.indicator.indicator

introduce cash to make a closed investment system, where netvalue analysis can be applied namely the totcftable only has one row at the very beginning

Parameters:
  • fundtradeobj – trade obj to be include
  • status – status table, if no trade obj is provided, it will include all fund based on code in status table
  • totmoney – positive float, the total money as the input at the beginning
  • cashobj – cashinfo object, which is designed to balance the cash in and out
unitvalue(date=datetime.datetime(2018, 8, 19, 0, 0))
Returns:float at unitvalue of the whole investment combination

xalpha.policy module

modules for policy making: generate status table for backtesting

class xalpha.policy.buyandhold(infoobj, start, end='2018-08-19', totmoney=100000)

Bases: xalpha.policy.policy

simple policy class where buy at the start day and hold forever, 始终选择分红再投入

status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.grid(infoobj, buypercent, sellpercent, start, end='2018-08-19', totmoney=100000)

Bases: xalpha.policy.policy

网格投资类,用于指导网格投资策略的生成和模拟。这一简单的网格,买入仓位基于均分总金额,每次的卖出仓位基于均分总份额。 因此实际上每次卖出的份额都不到对应原来档位买入的份额,从而可能实现更多的收益。

Parameters:
  • infoobj – info object, trading aim of the grid policy
  • buypercent – list of positive int or float, the grid of points when purchasing, in the unit of percent 比如 [5,5,5,5] 表示以 start 这天的价格为基准,每跌5%,就加一次仓,总共加四次仓
  • sellpercent – list of positive int or float, the grid of points for selling 比如 [8,8,8,8] 分别对应上面各买入仓位应该涨到的百分比从而决定卖出的点位,两个列表都是靠前的是高价位仓,两列表长度需一致
  • start – date str of policy starting
  • end – date str of policy ending
  • totmoney – 总钱数,平均分给各个网格买入仓位
status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.indicator_cross(infoobj, col, start, end='2018-08-19', totmoney=100000)

Bases: xalpha.policy.policy

制定两个任意技术指标之间(或和净值之间)交叉时买入卖出的策略。若收盘时恰好交叉,不操作,等第二日趋势确认。

Parameters:
  • info – info object, trading aim of the policy
  • col – a tuple with two strings, eg (‘netvalue’,’MA10’), when the left one is over the right one, we buy and otherwise we sell, that is the core of cross policy, you can choose any two columns as you like, as long as you generate them on the info object before input 也即左栏数据从下向上穿过右栏数据时,买入;反之亦然
  • start – date str of policy starting
  • end – date str of policy ending
  • totmoney – float or int, total money, in the cross policy, we dont have position division, instead we buy all or sell all on the given cross
status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.indicator_points(infoobj, start, col, buy, sell=None, buylow=True, end='2018-08-19', totmoney=100000)

Bases: xalpha.policy.policy

基于技术指标的策略生成类之一,给出技术指标的多个阈值,基于这些点数值进行交易

Parameters:
  • infoobj – info object, trading aim of the policy
  • col – str, stands for the tracking column of price table, eg. ‘netvalue’ or ‘PSY’
  • buy – list of tuple, eg [(0.1,1),(0.2,2),(0.3,5)]. buy 1/(1+2+5) of totmoney, when the col value approach 0.1 and so on.
  • sell – similar list of tuple as buy input. the difference is you can omit setting of sell list, this implies you don’t want to sell. 初始化不设置sell参数,在col设为netvalue时,用于进行金字塔底仓购买特别有效. 注意不论是 sell 还是 buy 列表,都要将更难实现(离中间值更远)的点位列在后边。比如如果现在是低买模式, 那么 buy 列表越考后的点数就越小。此外,不建议设置的买点卖点有重叠区域,可能会出现策略逻辑错误。
  • buylow – Bool, Ture 代表,对应点位是跌破买,涨破卖,如果是 False 则反之,默认是 True
  • start – date str of policy starting
  • end – date str of policy ending
  • totmoney – float or int, total money, in the points policy, we share them as different positions, based on the instruction of sell and buy list
status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.policy(infoobj, start, end='2018-08-19', totmoney=100000)

Bases: xalpha.record.record

base class for policy making, self.status to get the generating status table

Parameters:
  • infoobj – info object as evidence for policy making
  • start – string or object of date, the starting date for policy running
  • end – string or object of date, the ending date for policy running
  • totmoney – float or int, characteristic money value, not necessary to be the total amount of money
status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.scheduled(infoobj, totmoney, times)

Bases: xalpha.policy.policy

fixed schduled purchase for given date list

Parameters:
  • infoobj – info obj
  • totmoney – float, money value for purchase every time
  • times – datelist of datetime object for purchase date, eg [‘2017-01-01’,‘2017-07-07’,…] we recommend you use pd.date_range() to generate the schduled list
status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares
class xalpha.policy.scheduled_tune(infoobj, totmoney, times, piece)

Bases: xalpha.policy.scheduled

定期不定额的方式进行投资,基于净值点数分段进行投资

status_gen(date)

give policy decision based on given date

Parameters:date – date object
Returns:float, positive for buying money, negative for selling shares

xalpha.realtime module

module for realtime watch and notfication

xalpha.realtime.mail(title, content, sender=None, receiver=None, password=None, server=None, port=None, sender_name='sender', receiver_name=None)

send email

Parameters:
  • title – str, title of the email
  • content – str, content of the email, plain text only
  • conf – all other paramters can be import as a dictionay, eg.conf = {‘sender’: ‘aaa@bb.com’, ‘sender_name’:’name’, ‘receiver’:[‘aaa@bb.com’,’ccc@dd.com’], ‘password’:‘123456’, ‘server’:’smtp.bb.com’,’port’:123, ‘receiver_name’:[‘me’,’guest’]}. The receiver_name and sender_name options can be omitted.
class xalpha.realtime.review(policylist, namelist=None, date=datetime.datetime(2018, 8, 20, 0, 0))

Bases: object

review policys and give the realtime purchase suggestions

Parameters:
  • policylist – list of policy object
  • namelist – list of names of corresponding policy, default as 0 to n-1
  • date – object of datetime, check date, today is prefered, date other than is not guaranteed
notification(conf)

send email of self.content, at least support for qq email sender

Parameters:conf – the configuration dictionary for email send settings, no ** before the dict in needed. eg.conf = {‘sender’: ‘aaa@bb.com’, ‘sender_name’:’name’, ‘receiver’:[‘aaa@bb.com’,’ccc@dd.com’], ‘password’:‘123456’, ‘server’:’smtp.bb.com’,’port’:123, ‘receiver_name’:[‘me’,’guest’]}. The receiver_name and sender_name options can be omitted.
xalpha.realtime.rfundinfo(code)

give a fundinfo object with todays estimate netvalue at running time

Parameters:code – string of six digitals for funds
Returns:the fundinfo object
class xalpha.realtime.rtdata(code)

Bases: object

get real time data of specific funds

Parameters:code – string of six digitals for funds

xalpha.record module

module for status table IO

class xalpha.record.record(path='input.csv', **readkwds)

Bases: object

basic class for status table read in from csv file. staus table 是关于对应基金的申赎寄账单,不同的行代表不同日期,不同的列代表不同基金, 第一行各单元格分别为 date, 及基金代码。第一列各单元格分别为 date 及各个交易日期,形式 eg. 20170129 表格内容中无交易可以直接为空或0,申购为正数,对应申购金额(申购费扣费前状态),赎回为负数,对应赎回份额, 注意两者不同,恰好对应基金的金额申购份额赎回原则,记录精度均只完美支持一位小数。 几个更具体的特殊标记:

  1. 小数点后第二位如果是5,且当日恰好为对应基金分红日,标志着选择了分红再投入的方式,否则默认分红拿现金

2. 对于赎回的负数,如果是一个绝对值小于 0.005 的数,标记了赎回的份额占当时总份额的比例而非赎回的份额数目, 其中0.005对应全部赎回,线性类推。eg. 0.001对应赎回20%。

Parameters:
  • path – string for the csv file path
  • readkwds – keywords options for pandas.read_csv() function. eg. skiprows=1, skipfooter=2
sellout(date=datetime.datetime(2018, 8, 19, 0, 0), ratio=1)

Sell all the funds in the same ratio on certain day, it is a virtual process, so it can happen before the last action exsiting in the cftable, by sell out earlier, it means all actions behind vanish. The status table in self.status would be directly changed.

Parameters:
  • date – string or datetime obj of the selling date
  • ratio – float between 0 to 1, the ratio of selling for each funds

xalpha.remain module

provide class functions to adjust rem form data based on old rem form data such datastructure is useful when first-in-first-out mechanism considered in selling funds and it is also useful when converting the shares of funds.

as the nested list structure is very fragile and tend to induce unpredicatble behaviors, we strongly recommended anytime when rem data serves as function paramters, only utilize functions from this module

xalpha.remain.buy(remc, share, date)
Parameters:
  • remc – array of two-elements arrays, eg [[pd.Timestamp(), 50],[pd.Timestamp(), 30] the first element in tuple is pandas.Timestamp object for date while the second element is positive float for remaining shares, tuples in rem MUST be time ordered.
  • share – positive float, only 2 decimal is meaningful.
  • date – string in the date form or datetime object
Returns:

new rem after the buying

xalpha.remain.copy(remc)

copy the rem form data so that the return is independent of the input

xalpha.remain.sell(remc, share, date)
Returns:tuple, (sold rem, new rem) sold rem is the positions being sold while new rem is the positions being held
xalpha.remain.trans(remc, coef, date)

在基金份额折算时,将之前持有的仓位按现值折算,相当于前复权

Parameters:
  • coef – the factor shown in comment column of fundinfo().price, but with positive value
  • date – string in date form or datetime obj
Returns:

new rem after converting

xalpha.trade module

module for trade class

xalpha.trade.bottleneck(cftable)

find the max total input in the history given cftable with cash column

Parameters:cftable – pd.DataFrame of cftable
class xalpha.trade.trade(infoobj, status)

Bases: object

Trade class with fundinfo obj as input and its main attrs are cftable and remtable:

1. cftable: pd.Dataframe, 现金流量表,每行为不同变更日期,三列分别为 date,cash, share,标记对于某个投资标的 现金的进出和份额的变化情况,所有的份额数据为交易当时的不复权数据。基金份额折算通过流量表中一次性的份额增减体现。

2. remtable:pd.Dataframe, 持仓情况表,每行为不同变更日期,两列分别为 date 和 rem, rem 数据结构是一个嵌套的列表, 包含了不同时间买入仓位的剩余情况,详情参见 remain 模块。这一表格如非必需,避免任何直接调用。

Parameters:
  • infoobj – info object as the trading aim
  • status – status table, obtained from record class
briefdailyreport(date=datetime.datetime(2018, 8, 19, 0, 0))

quick summary of highly used attrs for trade

Parameters:date – string or object of datetime
Returns:dict with several attrs: date, unitvalue, currentshare, currentvalue
dailyreport(date=datetime.datetime(2018, 8, 19, 0, 0))

breif report dict of certain date status on the fund investment

Parameters:date – string or obj of date, show info of the date given
Returns:dict of various data on the trade positions
unitcost(date=datetime.datetime(2018, 8, 19, 0, 0))

give the unitcost of fund positions

Parameters:date – string or object of datetime
Returns:float number of unitcost
v_totvalue(end=datetime.datetime(2018, 8, 19, 0, 0), **vkwds)

visualization on the total values daily change of the aim

v_tradecost(start=None, end=datetime.datetime(2018, 8, 19, 0, 0), **vkwds)

visualization giving the average cost line together with netvalue line

Parameters:vkwds – keywords options for line.add()
Returns:pyecharts.line
v_tradevolume(**vkwds)

visualization on trade summary

Parameters:vkwds – keyword argument for pyecharts Bar.add(), and freq= label, please ref to the API of trade.vtradevolume function
Returns:pyecharts.bar
xirrrate(date=datetime.datetime(2018, 8, 19, 0, 0), guess=0.1)

give the xirr rate for all the trade of the aim before date (virtually sold out on date)

Parameters:date – string or obj of datetime, the virtually sell-all date
xalpha.trade.turnoverrate(cftable, end=datetime.datetime(2018, 8, 19, 0, 0))

calculate the annualized turnoverrate

Parameters:
  • cftable – pd.DataFrame of cftable
  • end – str or obj of datetime for the end date of the estimation
xalpha.trade.vtradevolume(cftable, freq='D', bar_category_gap='35%', **vkwds)

aid function on visualization of trade summary

Parameters:
  • cftable – cftable (pandas.DataFrame) with at least date and cash columns
  • freq – one character string, frequency label, now supporting D for date, W for week and M for month, namely the trade volume is shown based on the time unit
  • vkwds – keyword argument for pyecharts Bar.add()
Returns:

the Bar object

xalpha.trade.xirrcal(cftable, trades, date, guess)

calculate the xirr rate

Parameters:
  • cftable – cftable (pd.Dateframe) with date and cash column
  • trades – list [trade1, …], every item is an trade object, whose shares would be sold out virtually
  • date – string of date or datetime object, the date when virtually all holding positions being sold
  • guess – floating number, a guess at the xirr rate solution to be used as a starting point for the numerical solution
Returns:

the IRR as a single floating number