In this section, we will explore the design and implementation of quant trading strategies.
    Quant strategies are a precise set of rules that generate orders and manage the risk of your current position(量化交易是一套精准的规则，用于生成订单和管理当前头寸风险).
    Rules can be quite complex and are a product of systematic statistical analysis of an asset’s historical price data（对资产历史价格数据系统数据分析的产物）. You can use this analysis to uncover hidden patterns and behaviors in the market for particular asset or group of assets such as an index（指数）.

    Strategies range from low-frequency, at least by trading standards, to high-frequency. You must implement high-frequency trading strategies with software and market interfaces that can handle sub-millisecond order creation and submission.

    Quant strategies are also a type of forecasting method that attempts to predict the future value of a stock or other instrument where the direction of a price spread between two instruments.
    These forecasts are based on an observed price response to factors that you have identified as statistically significant in predicting changes in an instrument’s price.

    Mean reversion strategies（均值回归策略） depend on the stability or stationarity of an asset’s price or spread relative to another asset（依赖于一项资产的价格或相对于另一项资产的价差的稳定性或者平稳性）.
    When the price risk spread gets too high, it becomes more likely that it will decrease, and so revert to its long-term mean or neutral value. When the price risk spread gets too low, it becomes more likely that it will increase, and again revert to its mean. Correlation is a measure of how well two variables move together over time. Correlation coefficients(相关性系数) range from negative one or perfect negative correlation to zero, or no correlation to one which is perfect positive correlation.
    Positive correlation means that the variables move in tandem in the same direction while negative correlation means that they move in tandem but in opposite directions. When you look for a correlation in a price series, you normalize the prices so that each starts at 100 percent. The change in prices is then the cumulative return on each asset（每种资产的累计收益）. Notice how the returns in this example track closely, and then diverge by about 10 percent, and then by about 15 percent. These two assets are correlated, but the difference between their means is not stable. Co-integration tasks do not measure how well two variables move together, but rather whether the difference between their means remains constant. Often variables with high correlation will also be co-integrated and vice versa, but this isn’t always the case. In contrast to correlation when you test for co-integration, use prices rather than returns since you’re more interested in the trend between the variable’s means over time than in the individual price movements.

    In momentum or trend-following strategies, you’re just buying assets that had been past winners and, selling assets that had been past losers.（在动量或者趋势策略中，你只是买入过去的赢家的资产，卖出去亏损的资产） This is the opposite of a mean reversion strategy where you sell winners and buyback losers. Momentum strategies has been particularly popular over the last five years, and they outperformed the S&P 500 by a wide margin as you can see from this chart, which compares the performance of the iShare momentum ETF to the S&P.
    We will find possible explanations for this outperformance in both the underreaction and overreaction of prices to new market information（我们将从市场对价格反应不足和反应过度两个方面找到可能的解释）.

    Underreaction means it takes time for the market to fully incorporate new information（完全吸收新的信息）, and so the positive or negative effect is spread out over a longer time period. Overreaction means that the market tends to feed on its own positive or negative reaction. The market over rewards companies that release good news, and over punishes those that release bad news. You can also argue that it is inherently riskier to continue buying stocks after up-moves or selling them after down-moves.
    So momentum investors should get an extra return for taking this risk. This argument, however, is undercut by the fact that iShares Momentum ETF has a three-year beta of 0.91 versus 1.0 for the S&P 500. It is weakly supported however by the volatility of the momentum ETF being 12.5 percent versus 12.1 percent for the S&P. I think that the last argument is the strongest. Investors believe that momentum strategies will outperform the market, will cause them to bid up the prices of momentum stocks creating a positive reinforcement cycle（表现优于大盘）. Many traders believe that momentum stocks and also quality stocks are overvalued, and that this positive reinforcement cycle will unwind in a sharp sell-off of the stocks favored by these strategies.

    Market micro-structure is a combination of the physical trading infrastructure used by a trading platform and its participants, the platform’s trading rules, and the behaviors, and trading patterns of its participants（市场微观结构是指交易平台及其参与者所使用的实体交易设施、平台交易规则、参与者的交易行为和交易模式的组合）. The strategies used and abused by high-frequency traders had been described extensively in the media and books such as Flash Boys. High-frequency traders who attempt to capture an arbitrage profit by taking advantage of flaws and anomalies in this infrastructure that exists for a few milliseconds or less（高频交易者试图利用这个基础设施中存在几毫秒或者更少时间的缺陷和异常来获取套取利润）. They also seek to identify and exploit patterns in the trading behavior of their major competitors in each market（他们还试图寻求识别和和利用每个市场上主要竞争对手的交易行为模式）. These patterns are often identified using game theory models（博弈论模型）. Equally important is their ability to detect and profit from the execution of large orders that have been broken up and hidden using an order execution strategy.

    Let’s say a firm wants to purchase an unusually large quantity of stock（购买大量股票）. Now use an execution strategy that enters the trades in a way that is supposed to break up and hide the order but which unintentionally signals search strategy.
    Sophisticated traders have their own algorithms which are able to detect these stealth orders(秘密订单). They’re able to use this information to jump in front of the large orders, and profit by driving prices up ahead of a buy order or down ahead of a sell order（利用信息在大订单面前跃升，并通过在买入指令前推高价格或者在卖出指令前压低价格来获利）. Spoofing is where traders attempt to mislead other traders by submitting orders to the market, order book that they don’t intend to execute. Say a trading group needs to sell a stock and wants to get a better price. They can enter temporary large-sized buy orders below
    the current market bid to encourage other bidders to pay higher prices for the stock（他们可以低于当前市场的价格输入临时性的大宗买入指令，以鼓励其他竞购者为该股支付更高的价格）.