What is day trading?
Day trading (Day Trade) is a trading patterns. Mainly it refers to the short holding period, leaving the overnight trading positions. Day trading after the stock market can immediately capture trading opportunities from the market costs, profit after the market if not immediately, ready to leave quickly. Because this trading city in a short time, so the lower risk tolerance of market fluctuations. Currently, worldwide there are some master manipulator of transactions made using this stable earnings, have been successful.
Intraday market strategy in China feasible?
With different foreign markets, China's stock market at this stage the implementation of "T + 1" deal, on this basis of "T + 0" operation, must be the bottom compartment of buy low sell high.
Whenever rich and have a high winning percentage in intra-day trading on experienced players in the holding period, will have a high interest in intra-day trading, transaction frequency will be higher. Above statements have implied a hidden premise: Must be a day not prepared to sell to those who do T, otherwise it will spread to a few minor points but worth the candle.
Day trading because of its rapid spread settled and smaller features, the first condition that is a profitable trader trading back and forth to spread greater than the transaction costs. Calculated in accordance with the current standard brokerage commissions Wansan widely used, the cost of a complete transaction friction cycle of slightly less than 0.2%, this point of view, such low profitable day trading commission becomes relatively easy. According to my own trading experience, as long as the participants of a single earnings expectations reasonable and intervention at the point of time is not to stray too far from, you can get a lot of money to make a positive return in the long T career.
Policy implementation (Denver-based quantification platform)
Tactical Thought
This strategy is first to buy "SHSE.600000" stock 10,000 shares.
Calculated "MACD (12,26,9)" according to the line data 300s.
"MACD" MACD underwater, with the average time of a long array of buy 1,000 shares, "MACD" Water Sicha, with the average short order, when to sell 1,000 shares.
The number of shares does not exceed the daily operations of the original position, and prior to closing the position to adjust positions before the opening bell.
The main steps to implement strategies
Subscription data
Subscribe (Symbols = Symbols, Frequency = '300S', COUNT = 35, wait_group = True)
(Symbols = Symbols, Frequency = '300S', COUNT = 35, wait_group = True)
the subscription data need to define the init function is provided inside, and call the subscribe function:
symbols need to set the underlying code for subscriptions.
frequency to be set subscription data cycle level, set here expressed 1d period of one day.
Count the number of bar needs to be set acquired
data collection
= context.data recent_data (= Symbol Symbol, Frequency = '300S', COUNT = 35, = Fields 'Close')
= context.data (= Symbol Symbol, Frequency = '300S', COUNT = 35, = Fields 'Close' )
after the subscription data, need to get data already subscribed to operate, then the need to call context.data function:
symbols need to set the underlying code for subscriptions.
frequency to be set subscription data cycle level, set here expressed 1d period of one day.
Count the number of bar needs to be set acquired
fields need to set the type of the return value
Get the current time bar of
on_bar DEF (context, Bars):
bar Bars = [0]
Day bar.bob.strftime = ( '% Y-M-% D%% H:% M:% S')
on_bar (context, Bars):
bar = Bars [0]
day bar.bob.strftime = ( '% Y-M-% D%% H:% M:% S')
in on_bar function, you need to determine whether the current bar is the last day of a transaction, to determine whether the open, where information can be passed directly past the bar.
Backtesting report
analysis
We selected from January 2016 to July 2016 as a back-test cycle, Poly Real Estate (600,048) as the underlying stock can be seen:
Winning (profitable than the number of open positions with the total number of open positions) reached 46%, of course, as you need to develop open open condition other high winning percentage.
Kama ratio (annualized rate of return than the maximum retracement of history) is the maximum rate retracement to measure risk. Using the maximum rate retracement to measure risk, concern is the most extreme case. Kama ratios indicate higher remuneration policy to withstand the maximum loss per unit obtained. Li Kama in this ratio reached 1.4.
Sharpe ratio (annualized rate of return minus the risk-free rate of return than the difference between the rate of return volatility) reached 0.78.
Strategy yield curve and the underlying stock returns has great relevance, the key point is to control intra-day trading fee, while increasing the odds, maximize profit and loss ratio, making the spread income loss is greater than the closing fee.
+0 stock trading strategies Source:
# coding=utf-8
from __future__ import print_function, absolute_import, unicode_literals
try:
import talib
except:
print('请安装TA-Lib库')
from gm.api import *
'''
本策略首先买入SHSE.600000股票10000股
随后根据60s的数据来计算MACD(12,26,9)线,并在MACD>0的时候买入100股,MACD<0的时候卖出100股
但每日操作的股票数不超过原有仓位,并于收盘前把仓位调整至开盘前的仓位
回测数据为:SHSE.600000的60s数据
回测时间为:2017-09-01 08:00:00到2017-10-01 16:00:00
'''
def init(context):
# 设置标的股票
context.symbol = 'SHSE.600000'
# 用于判定第一个仓位是否成功开仓
context.first = 0
# 订阅浦发银行, bar频率为1min
subscribe(symbols=context.symbol, frequency='60s', count=35)
# 日内回转每次交易100股
context.trade_n = 100
# 获取昨今天的时间
context.day = [0, 0]
# 用于判断是否触发了回转逻辑的计时
context.ending = 0
def on_bar(context, bars):
bar = bars[0]
if context.first == 0:
# 最开始配置仓位
# 需要保持的总仓位
context.total = 10000
# 购买10000股浦发银行股票
order_volume(symbol=context.symbol, volume=context.total, side=PositionSide_Long,
order_type=OrderType_Market, position_effect=PositionEffect_Open)
print(context.symbol, '以市价单开多仓10000股')
context.first = 1.
day = bar.bob.strftime('%Y-%m-%d')
context.day[-1] = day[-2:]
# 每天的仓位操作
context.turnaround = [0, 0]
return
# 更新最新的日期
day = bar.bob.strftime('%Y-%m-%d %H:%M:%S')
context.day[0] = bar.bob.day
# 若为新的一天,获取可用于回转的昨仓
if context.day[0] != context.day[-1]:
context.ending = 0
context.turnaround = [0, 0]
if context.ending == 1:
return
# 若有可用的昨仓则操作
if context.total >= 0:
# 获取时间序列数据
symbol = bar['symbol']
recent_data = context.data(symbol=symbol, frequency='60s', count=35, fields='close')
# 计算MACD线
macd = talib.MACD(recent_data['close'].values)[0][-1]
# 根据MACD>0则开仓,小于0则平仓
if macd > 0:
# 多空单向操作都不能超过昨仓位,否则最后无法调回原仓位
if context.turnaround[0] + context.trade_n < context.total:
# 计算累计仓位
context.turnaround[0] += context.trade_n
order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Long,
order_type=OrderType_Market, position_effect=PositionEffect_Open)
print(symbol, '市价单开多仓', context.trade_n, '股')
elif macd < 0:
if context.turnaround[1] + context.trade_n < context.total:
context.turnaround[1] += context.trade_n
order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Short,
order_type=OrderType_Market, position_effect=PositionEffect_Close)
print(symbol, '市价单平多仓', context.trade_n, '股')
# 临近收盘时若仓位数不等于昨仓则回转所有仓位
if day[11:16] == '14:55' or day[11:16] == '14:57':
position = context.account().position(symbol=context.symbol, side=PositionSide_Long)
if position['volume'] != context.total:
order_target_volume(symbol=context.symbol, volume=context.total, order_type=OrderType_Market,
position_side=PositionSide_Long)
print('市价单回转仓位操作...')
context.ending = 1
# 更新过去的日期数据
context.day[-1] = context.day[0]
if __name__ == '__main__':
'''
strategy_id策略ID,由系统生成
filename文件名,请与本文件名保持一致
mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
token绑定计算机的ID,可在系统设置-密钥管理中生成
backtest_start_time回测开始时间
backtest_end_time回测结束时间
backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
backtest_initial_cash回测初始资金
backtest_commission_ratio回测佣金比例
backtest_slippage_ratio回测滑点比例
'''
run(strategy_id='strategy_id',
filename='main.py',
mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-09-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
backtest_adjust=ADJUST_PREV,
backtest_initial_cash=2000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)