Could you add a sample code for alpha 101 formula?

Could you add a sample code for alpha 101 formula?

I have bought the book “advanced trading strategies”. But I want to use the qstrader to write the sample code for worldquant alpha 101’s formula. I am not familiar the qstrader. I don’t know how to get the dataframe from 100 stock’s yahoo price. And I don’t know how to write the position sizer to reach the goal. Could you write the simpliest formula 101 on top 100 capital stocks? Alpha#101: ((close - open) / ((high - low) + .001))

It might use 2week or 1 month rebalance day that will be simple. The position size will use the profolio value to buy 100 stocks.

Thank you very much.

Uqer’s platform’s sample code:

import numpy as np

start = '2010-01-01'                       # 回测起始时间

end = '2016-03-20'                         # 回测结束时间

benchmark = 'HS300'                        # 策略参考标准

universe = set_universe("HS300")           # 证券池，支持股票和基金

capital_base = 100000                      # 起始资金

freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测

refresh_rate = 20                          # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

​
def foo(data, dependencies=['closePrice', 'openPrice', 'highPrice', 'lowPrice'], max_window=1):
    today = (data['closePrice'].ix[-1]-data['openPrice'].ix[-1])/((data['highPrice'].ix[-1]-data['lowPrice'].ix[-1]) + 0.001)
    return today



def initialize(account):                   # 初始化虚拟账户状态
    a = Signal("worldquant_101", foo)
    account.signal_generator = SignalGenerator(a)



def handle_data(account):                  # 每个交易日的买入卖出指令



    # 只选信号，即worldquant_101计算结果大于0的股票，而且选前40只
    # 平均仓位持仓
    weight = account.signal_result['worldquant_101']
    weight = weight[weight>0]
    weight.sort(ascending=False)
    weight = weight[0:40]
    weight = weight/weight.abs()
    weight = weight/weight.sum()
    weight = weight.replace([np.inf, -np.inf], np.nan).dropna()



    buy_list = weight.index
    sell_list = account.valid_secpos

    for stk in sell_list:
        if stk not in buy_list:
            order_to(stk, 0)



    total_money = account.referencePortfolioValue
    prices = account.referencePrice 

    for stk in buy_list:
        if stk not in prices:
            continue

        if np.isnan(prices[stk]) or prices[stk] == 0:  # 停牌或是还没有上市等原因不能交易
            continue

        order_num = int(total_money * weight[stk] / prices[stk] /100)*100
        if order_num < 100:
            order_num = 100
        order_to(stk, order_num)

[DirkFR]

There’s a fairly active thread on the 101 Formulaic Alphas at Quantopian (not sure if Mike approves of the link to them…): https://www.quantopian.com/posts/the-101-alphas-project Towards the bottom of the dicussion, there’s an implementation of at least 50 or so of the Alphas. General consensus is not so positive and given the factor weights that come with some of the Alphas, they seem (to me at least) highly optimized/fitted: Alpha#69: ((rank(ts_max(delta(IndNeutralize(vwap, IndClass.industry), 2.72412), 4.79344))^Ts_Rank(correlation(((close * 0.490655) + (vwap * (1 - 0.490655))), adv20, 4.92416), 9.0615)) * -1) – I mean, seriously?

[easytrader]

I use Shanghai 50 stocks to produce alpha 101’s formula.

You should type the cmd: python twentydays_liquidate_rebalance1.py --tickers=000001SS,600000SS,600016SS,600030SS,600050SS,600111SS,600519SS,600795SS,600893SS,601006SS,601169SS,601288SS,601336SS,601601SS,601669SS,601800SS,601901SS,601989SS,600010SS,600018SS,600036SS,600104SS,600150SS,600585SS,600837SS,600958SS,601088SS,601186SS,601318SS,601390SS,601628SS,601688SS,601818SS,601985SS,601998SS,600015SS,600028SS,600048SS,600109SS,600518SS,600637SS,600887SS,600999SS,601166SS,601211SS,601328SS,601398SS,601668SS,601766SS,601857SS,601988SS > log5.txt

You can see the figure below:

If you find the bug, please tell me. Thank you very much.