0. 第一个量化策略
# 初始化函数，设定基准等等
def initialize(context):
set_benchmark(\'000300.XSHG\')
g.security = get_index_stocks(\'000300.XSHG\') # 股票池
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
log.set_level(\'order\',\'warning\')
def handle_data(context, data):
# 一般情况下先卖后买
tobuy = []
for stock in g.security:
p = get_current_data()[stock].day_open
amount = context.portfolio.positions[stock].total_amount
cost = context.portfolio.positions[stock].avg_cost
if amount > 0 and p >= cost * 1.25:
order_target(stock, 0) # 止盈
if amount > 0 and p <= cost * 0.9:
order_target(stock, 0) # 止损
if p <= 10.0 and amount == 0:
tobuy.append(stock)
if len(tobuy)>0:
cash_per_stock = context.portfolio.available_cash / len(tobuy)
for stock in tobuy:
order_value(stock, cash_per_stock)
1. 双均线策略
def initialize(context):
set_benchmark(\'600519.XSHG\')
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
g.security = [\'600519.XSHG\']
g.p1 = 5
g.p2 = 30
def handle_data(context, data):
for stock in g.security:
# 金叉：如果5日均线大于10日均线并且不持仓
# 死叉：如果5日均线小于10日均线并且持仓
df = attribute_history(stock, g.p2)
ma10 = df[\'close\'].mean()
ma5 = df[\'close\'][-5:].mean()
if ma10 > ma5 and stock in context.portfolio.positions:
# 死叉
order_target(stock, 0)
if ma10 < ma5 and stock not in context.portfolio.positions:
# 金叉
order_value(stock, context.portfolio.available_cash * 0.8)
# record(ma5=ma5, ma10=ma10)
2. 因子选股
def initialize(context):
set_benchmark(\'000002.XSHG\')
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
g.security = get_index_stocks(\'000002.XSHG\')
g.q = query(valuation).filter(valuation.code.in_(g.security))
g.N = 20
run_monthly(handle, 1)
def handle(context):
df = get_fundamentals(g.q)[[\'code\', \'market_cap\']]
df = df.sort(\'market_cap\').iloc[:g.N,:]
to_hold = df[\'code\'].values
for stock in context.portfolio.positions:
if stock not in to_hold:
order_target(stock, 0)
to_buy = [stock for stock in to_hold if stock not in context.portfolio.positions]
if len(to_buy) > 0:
cash_per_stock = context.portfolio.available_cash / len(to_buy)
for stock in to_buy:
order_value(stock, cash_per_stock)
3. 多因子选股
def initialize(context):
set_benchmark(\'000002.XSHG\')
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
g.security = get_index_stocks(\'000002.XSHG\')
g.q = query(valuation, indicator).filter(valuation.code.in_(g.security))
g.N = 20
run_monthly(handle, 1)
def handle(context):
df = get_fundamentals(g.q)[[\'code\', \'market_cap\', \'roe\']]
df[\'market_cap\'] = (df[\'market_cap\'] - df[\'market_cap\'].min()) / (df[\'market_cap\'].max()-df[\'market_cap\'].min())
df[\'roe\'] = (df[\'roe\'] - df[\'roe\'].min()) / (df[\'roe\'].max()-df[\'roe\'].min())
df[\'score\'] = df[\'roe\']-df[\'market_cap\']
df = df.sort(\'score\').iloc[-g.N:,:]
to_hold = df[\'code\'].values
for stock in context.portfolio.positions:
if stock not in to_hold:
order_target(stock, 0)
to_buy = [stock for stock in to_hold if stock not in context.portfolio.positions]
if len(to_buy) > 0:
cash_per_stock = context.portfolio.available_cash / len(to_buy)
for stock in to_buy:
order_value(stock, cash_per_stock)
4. 均值回归
import jqdata
import math
import numpy as np
import pandas as pd
def initialize(context):
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
set_benchmark(\'000002.XSHG\')
g.security = get_index_stocks(\'000002.XSHG\')
g.ma_days = 30
g.stock_num = 10
run_monthly(handle, 1)
def handle(context):
sr = pd.Series(index=g.security)
for stock in sr.index:
ma = attribute_history(stock, g.ma_days)[\'close\'].mean()
p = get_current_data()[stock].day_open
ratio = (ma-p)/ma
sr[stock] = ratio
tohold = sr.nlargest(g.stock_num).index.values
# print(tohold)
# to_hold = #
for stock in context.portfolio.positions:
if stock not in tohold:
order_target_value(stock, 0)
tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
if len(tobuy)>0:
cash = context.portfolio.available_cash
cash_every_stock = cash / len(tobuy)
for stock in tobuy:
order_value(stock, cash_every_stock)
5. 布林带策略
#import numpy as np
#import pandas as pd
def initialize(context):
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
set_benchmark(\'600036.XSHG\')
g.security = \'600036.XSHG\'
g.M = 20
g.k = 2
# 初始化此策略
def handle_data(context, data):
sr = attribute_history(g.security, g.M)[\'close\']
ma = sr.mean()
up = ma + g.k * sr.std()
down = ma - g.k * sr.std()
p = get_current_data()[g.security].day_open
cash = context.portfolio.available_cash
if p < down and g.security not in context.portfolio.positions:
order_value(g.security, cash)
elif p > up and g.security in context.portfolio.positions:
order_target(g.security, 0)
6. PEG策略
import jqdata
import pandas as pd
def initialize(context):
set_benchmark(\'000300.XSHG\')
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
g.security = get_index_stocks(\'000300.XSHG\')
g.N = 20
g.q = query(valuation.code, valuation.pe_ratio, indicator.inc_net_profit_year_on_year).filter(valuation.code.in_(g.security))
run_monthly(handle, 1)
def handle(context):
df = get_fundamentals(g.q)
df = df[(df[\'pe_ratio\']>0) & (df[\'inc_net_profit_year_on_year\']>0)]
df[\'peg\'] = df[\'pe_ratio\'] / df[\'inc_net_profit_year_on_year\'] / 100
df = df.sort(columns=\'peg\')
tohold = df[\'code\'][:g.N].values
# tohold = #
for stock in context.portfolio.positions:
if stock not in tohold:
order_target_value(stock, 0)
tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
if len(tobuy)>0:
cash = context.portfolio.available_cash
cash_every_stock = cash / len(tobuy)
for stock in tobuy:
order_value(stock, cash_every_stock)
7. 羊驼交易法则
import jqdata
import pandas as pd
def initialize(context):
set_benchmark(\'000002.XSHG\')
set_option(\'use_real_price\', True)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type=\'stock\')
g.security = get_index_stocks(\'000002.XSHG\')
g.period = 30
g.N = 10
g.change = 1
g.init = True
run_monthly(handle, 1)
def get_sorted_stocks(context, stocks):
df = history(g.period, field=\'close\', security_list=stocks).T
print(df)
df[\'ret\'] = (df.iloc[:,len(df.columns)-1] - df.iloc[:,0]) / df.iloc[:,0]
df = df.sort(columns=\'ret\', ascending=False)
return df.index.values
def handle(context):
if g.init:
stocks = get_sorted_stocks(context, g.security)[:g.N]
cash = context.portfolio.available_cash * 0.9 / len(stocks)
for stock in stocks:
order_value(stock, cash)
g.init = False
return
stocks = get_sorted_stocks(context, context.portfolio.positions.keys())
for stock in stocks[-g.change:]:
order_target(stock, 0)
stocks = get_sorted_stocks(context, g.security)
for stock in stocks:
if len(context.portfolio.positions) >= g.N:
break
if stock not in context.portfolio.positions:
order_value(stock, context.portfolio.available_cash * 0.9)