自动化算法交易平台和定量投资策略分享
首先简单自我介绍，我06年北大毕业，然后在国内从事证券交易，10年到哥大攻读金融数学硕士，现在在纽约从事quantitativetrading方面的研究工作。我在MACD算是新人，希望能跟大家一起交流学习，希望前辈多多指教。我想大家对Automated Algorithmic Trading应该都有一定的了解了。这次想跟大家推荐的是我们公司研发的全球第一款，也是现在唯一的一个为普通投资者开放的程序化算法交易平台。这个交易平台给不是很精通算机编程和复杂数学统计模型的交易员和投资者提供了一个可以自己创建复杂算法并实现自动化程序交易的平台。用户可以通过简单的可视化编程，在平台上对自己构建的交易策略,在历史数据上的进行backtest, 实时模拟，还有交易。
平台现在处于最后的内测阶段，并开始限时发放第一批免费账号。
获取步骤：
1.
为了您的个人信息安全及防止它人获取你的邮件地址发送垃圾邮件，请以站内信息或者直接邮件的方式，把您的姓+名+邮箱地址发送给我。（邮件，标题注明“算法交易”）如果不愿意透露您的真实姓名，可以用英文名代替，例如Jack Chen。
2.
我帮你内部系统注册。
3.
平台上线时您的邮箱会收到平台的登录连接和密码，then enjoy。
不过请注意，这是全英文系统，现阶段只能交易美国NYSE和NASDAQ股票。
接下来，如果您还不是很清楚什么是自动化算法交易，您可能会想想看一下下面的简单介绍和实例。
算法交易(algorithmic trading)是指事先设计好交易策略，然后将其编制成计算机程序。利用计算机程序的算法来决定交易下单的时机、价格和数量等。程序化下单能避免人的非理性因素造成的干扰，并能更精确的下单。并能同时管理大量的操作，自动判断将大单分拆为小单，减小冲击成本。相比于手动下单交易而言，算法交易具有一系列的优势。主要体现在减少冲击成本、自动监控交易机会，隐蔽交易意图。还可以寻求最佳的成交执行路径，得到市场最好的报价；算法交易还能避免人的非理性因素造成的干扰；快速分析多种技术指标和复杂数学模型，捕捉瞬间的利机会，更精确地下单。在现美国80%以上的hedge-fund或者propfirm都已经在用这种自动化程序化的方式在交易。这样的程序化交易已经存在了十几年了，因为需要大量的专业技术支持，普通投资者是无法做到的。
下面给大家简单举一个简单的算法交易的例子
The strategyHere we introduce a trendfollowing strategy utilizing Average Directional Index and Moving Average.
This isa daily two sided strategy
Buy(Sell) to open the Long (Short) position when the both conditions are met
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Thedaily change of 18day-ADX value is greater than +1
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The3-day MA cross above (down) the 20-day MA
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Thestock price is above (below) its 250-day MA
Sell(Buy) to close long (short) position when the following conditions is met
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Thestock price cross below (above) its 250-day MA
Test ResultWe back-tested this strategy on S&P500component stocks from 1/3/2002 to 7/5/2012. We set the trading parameters asfollows:
Total amount of cash available toinvest = $1,000,000
Investment size for each trade =$3,000
Transaction cost = 0.5 % oftransaction value
Finally we obtained the testresult:
Performance Comparison
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**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**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Statistics
Buy SPY andhold
Our ADX+MAStrategy
Initial Investment (1/2/2001)
$1,000,000
$1,000,000
Final Portfolio Value (7/5/2012)
$1,173,616
$2,110,847
Average Yearly Return
1.536%
7.374%
Total Number of Trades
N/A
6736
Total Number of Wins
N/A
2216
Winning Ratio
N/A
32.9%
Average Profit per Win-Trade
N/A
$1,100.28
Average Loss per Lose-Trade
N/A
-$292.04
这个策略非常简单，是一个低频的daily技术指标交易模型，只是简单的跑赢市场而已，这里只是给大家提供一个算法交易的例子供大家参考和了解。我相信大家一定能改进这个策略，改变参数，提高交易频率，或是用到更好的统计交易模型，挖掘更好的利润。
先写到这里，如果有问题，我非常愿意跟大家讨论。
谢谢！ 楼主是个好人 做网站的，大家加油！！
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