技术分析介绍
亚当·索拉博
国际技术分析联盟主席，CQS技术分析总监
北京
2011年9月6日
内容
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什么是技术分析
技术分析的历史
技术分析 对 基本面经济/金融分析
技术分析研究的对象及其假设
技术分析的类型（图表、振荡指标、型态与分形）
趋势、反转、周期
运用技术分析进行交易与投资：关于概率以及出市入市的价位
对技术分析之批判
附录——推荐阅读资料及学术参考
2
什么是技术分析？

“技术分析是指分析证券历史价格从而预测未来价格走势的过程。”
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市场预测只建立在价格、时间、成交量、未平仓合约信息的基础之上
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而忽略如盈利、现金流、资产负债表等基本面数据
3
技术分析的历史
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技术分析的起源可追溯到几百年前
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已知的最古老的技术分析方法由本间宗久（Munehisa Homma）所发明，并在18
世纪早期应用于大米市场
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技术分析在美国的发展可追溯到查尔士·道（Charles H. Dow）从1900年开始在
华尔街日报发表的技术分析论文
4
技术分析的历史
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从此以后，更多的技术分析工具得到了发展，而且越来越强调计算机辅助技术。
5
技术分析 VS 基本面经济/金融分析
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基本面分析利用宏观和微观的经济数据来预测国家、企业、个人的宏观和微观结
果。
– 他们使用基本面数据来构造未来现金流和投资回报的模型。
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技术分析则使用价格、时间、交易活动数据，来预测未来价格范围和活动的概率。
– 我们使用技术数据来建立交易和投资策略。
6
技术分析的研究对象
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价格=任何交易活跃的证券、货币或大宗商品的价格历史
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时间=年度、月度、每周、每日、日间数据
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交易数据=成交量以及未平仓合约
7
技术分析的假设
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市场价格反映了任何时刻的所有已知信息。
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市场价格会对额外的信息做出反应。
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市场参与者不断变化的情绪和活动会留下一连串变化着的价格和市场活动的踪迹，
用技术分析为之建造模型，可预测未来的价格范围和变动。
8
技术分析的类型
（图表、振荡指标、型态与分形）
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重要价位（依重要程度排列）
– 1）现价=当前的交易价
– 2）收市价=某一日（或其他时段）交易活动结束前最后一
笔交易的成交价格
– 3）开市价=某时段开始的价格
– 4）最高价=该时段内最高的成交价格；最低价=该时段内
最低的成交价格
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收市价、开市价、最高价、最低价
–
收市价=一日（或其他时段）交易活动结束前最后一笔交易的成交价格
–
开市价=某交易时段开始的价格
–
最高价=该时段内最高的成交价格
–
最低价=该时段内最低的成交价格
最高价 = 2.5
在这个交易时段里，市场开市价为2.2，最
高价为2.5，最低价为1.5，最后收于1.8。
开市价 = 2.2
收市价= 1.8
技术分析师在为下个交易时段制定交易策
略时会考虑这些价格水平。
最低价= 1.5
10
图表的类型
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线形图
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柱状图
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K 线图
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OX图
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市场概览 (又译四度空间)
11
线形图
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线形图只使用收盘价的历史记录
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这类型的图包含了有限但关键的信息
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就价格历史而言，长期数据在半对数刻度中有时候会更有用。
1992年来上海A股指数
1992年来上海A股指数
算术标度
半对数标度
12
支撑
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支撑 = 预期购买活动集中的价格水平
–
支撑水平可在一系列技术指标基础上识别出来
–
在先前价格最低位或者“突破”的水平上常常能看到支撑现象
上海A股指数-2008年8月-2011年6月
横向支撑
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阻力
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阻力 = 预期出售活动集中的价格水平
–
支撑水平可在一系列技术指标基础上识别出来
–
在先前价格最高位或者“突破”的水平上常常能看到阻力现象
上海A股指数-2008年8月-2011年6月
横向阻力
14
柱状图
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柱状图包含开市价、最高价、最低价、收市价的信息。
柱状图展示了每个交易时段的价格范围和清晰度。
最高价 = 2.5
开市价 = 2.2
收市价 = 1.8
最低价 = 1.5
2006年以来S&P500指数
算术标度
15
K线图
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阴阳烛图包含开市价、最高价、最低价、收市价的信息。
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用不同的颜色标识上升或下跌的交易日，从而强调每个市场时段的不同心理。
连续几个阴阳烛的组合能有效反映变化
着的市场心理
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阴阳烛图
2011年2月以来欧元兑美元
顶部三星——熊市形态
锤头——牛市形态
前进白色三兵——牛市形态
17
OX图
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点数图忽略时间因素，只记录交易价格水平的实质变化。
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点数图为技术分析师提供了忽略时间因素而分析市场行为的另一种途径。
2006年6月以来黄金走势
点数图凸显支撑和阻力水平
18
市场概览
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市场轮廓图以时间分配的形式记录成交价格，一个交易时段里不同分段的每
个成交价格用不同的字母来表示。
19
市场概览
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市场概览图对于在证券交易所交易的证券尤为有效，每个成交价格的成交量都可
以得到利用。
20
成交量分析
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成交量是某一特定交易时段或价格水平上成交的证券价值。
2008年6月以来的Google
价格未达到预期高位，人们纷纷抛
售，导致成交量出现峰值
价格在$350左右时，买入者涌入市场，导致成交量增大
21
未平仓量分析
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未平仓量是指某特定市场在某交易时段结束时，多方所持有或空方所抛空的契约
口数。
2011年2月以来的可可期货
下跌趋势加剧，未平仓量增加，从而强
化熊市预期
市场参与者获利，未平仓量减少
22
趋势分析
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市场上存在趋势。顺应趋势而持有的仓位比逆势而为更有可能获得盈利。
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技术分析师试图使用几何和数学技术来识别市场的趋势及其范围。
2002年-2009年S&P 500指数
出现上升趋势前
有好几个买入机
会
23
2008年8月持续已
久的上涨趋势出
现转折，标志着
大跌的开始
趋势分析

技术分析师可使用移动平均数来使数据平滑化，从而识别趋势和趋势变动。
2002年-2009年S&P 500指数
2008年8月长期上
升趋势戛然而止，
慢速快速移动平
均线的交叉预示
着跌势到来
从200天移动平均价格
可看出上升趋势，在上
升趋势出现前有很多买
入机会
24
反转分析
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每个趋势结束时会发生反转，市场先前的方向动摇不定，趋势的变化日益明显。
技术分析师试图识别常见的反转形态以及反转市场行为的信号，从而察觉市场方
向改变的预警。
2000年7月至2001年6月的黄金
牛势锤子形态和双重谷底标志着2000年黄
金市场熊势的反转，当时价格为255美元
25
形态分析
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随着时间的推移，市场往往会呈现常见的行为形态。
技术分析师试图识别常见的价格形态，从而制定利用这些价格形态的交易策略。
无法突破先前更
高价格——熊势
形态
旗形是“持续形态”，
预示着市场重拾先前趋
势前会有一段“整固”
形态。
牛势“倒置头肩形态”
预示先前熊市的结束
成功“突破”标志着上
升趋势的持续
26
分形分析
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随着时间的推移，趋势通常会呈现出常见的价格形态和分形行为。
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技术分析师试图识别价格分形，利用某特定趋势或回调的演变来制定一些交易系
统。
27
分形分析

1996年至2009年标普500的艾略特波浪图
28
周期分析
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所有市场、多重时间段都会呈现出周期性。
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技术分析师试图识别市场周期，从而制定一些交易系统来利用每个周期以及该周
期与其他时间周期之间可能存在的谐波。
29
振荡指标
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振荡指标把历史上的价格信息放进数学模型中，使数据平滑化，同时突出动能和
市场特征的改变。
相对强弱指数从70%以上跌到70%以下=熊市信号
从2010年7月至2011年6月日元兑美元
相对强弱指数从30%以下上升至30%以上=牛市信号
30
振荡指标
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有很多不同的振荡指标研究（不下一百种）
每一种研究都试图突出价格和市场行为的不同方面
有些更适合趋势市场，有些更适合横向市场
有些振荡指标甚至寻求比较趋势市场和横向市场，比如动向指数（DMI）
2010年7月至2011年7月黄金的动向指数图表
动向指数预示
着不断加强的
上升趋势
动向指数预示
着不断加强的
上升趋势
动向指数一开始预示
减弱的上升趋势，而
后转为较弱的下降趋
势
31
不断减弱的
上升趋势
利用技术分析做交易
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技术分析师使用一系列的技巧，从而达到如下目标：

判断当前市场行为的特性——趋势，调整，抑或反转

识别适合持有新的多头或空头仓位的关键市场价位；既呈现出良好的成功概率，
又有有效平衡盈利潜力与止损的风险回报比率

对所有交易仓位进行风险管理，以后市价格变动为借鉴，根据变化中的市场条件
调整持续的技术策略
32
利用技术分析做交易
技术分析师交易原则 #1 – 趋势是你的朋友 – 2010年2月在1050美元的价位
上购买黄金



判断当前市场行为的特性—市场上升趋势明显，有卖空迹象。
识别重要的市场水平–购进多头仓位的时机应该是：上升趋势低谷附近@1040美
元，以及向上倾斜的移动平均线@1024美元，因为随后市价将恢复到原来的上升
趋势，从而带来大笔盈利。
风险管理 – 下止损单的位置应该比趋势线和移动平均线稍低@1010美元，若升势
结束会有小额损失。
33
利用技术分析做交易
技术分析师交易原则 #1 – 趋势是你的朋友 – 2010年2月在1050美元的价位上购买黄
金
 潜在盈利
–
–

潜在损失
–
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77/40 （近 2:1）@1127
111/40 （近 2.8 : 1） @1161
175/40 （近 4.4 : 1）@1225
不同概率带来的结果
–
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若在1010美元价位上实现止损，则损失40美元
风险回报比率
–
–
–
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接下来的高价分别是1127美元、1161美元、1225美元
若在1050美元价位上购入，则潜在盈利分别是77美元、111美元、175美元
如果就第一个价格目标而言，只有50%实现了上述交易，那么每次交易平均盈利仍为37美元
通过设置特定的入市和离市的价位，技术分析师就能利用这些概率并从中获益。
34
利用技术分析做交易
技术分析师交易原则 #1 – 趋势是你的朋友 – 2010年2月在1050美元的价位上购
买黄金
后来也出现大量类似的风险/回报技术性购入信
号，颇具吸引力
2010年2月 @ $1050
35
技术分析的限制

依赖准确及时的市场数据

在交易活跃的市场最为有效

有些技巧较为主观
–

视觉技巧和数学技巧都会应用到
技术分析不是“水晶球”
–
技术分析师无法预知未来。他们只是尝试在过去的价格和市场历史基础上，估计未来发展的概率，
从而判别入市离市的明智时机。
36
谢谢
www.ifta.org
Adam Sorab – IFTA President
Head of Technical Research
CQS (UK) LLP
5th Floor
Tel:
33 Grosvenor Place Fax:
London SW1X 7HY
Mob:
United Kingdom
Web:
+44 20 7201 2499
+44 20 7201 1161
+44 (0)7785 341 991
ww.cqs.ch
附录1：推荐书目
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Murphy, John J. - Technical Analysis of the Financial Markets - New York Institute of Finance/Prentice
Hall 1999
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du Plessis, Jeremy - The Definitive Guide to Point and Figure - Harriman House Ltd.
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Plummer, Tony - Forecasting Financial Markets - Kogan Page 1998
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Prechter, Robert - R N Elliott's Masterworks, the Definitive Collection - New Classic Library, a division
of Elliott Wave International, Gainesville GA, USA 1994
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Lambert, Clive - Candlestick Charts - Harriman House Ltd.

Linton, David - Cloud Charts, Trading Success with the Ichimoku Technique - Updata, Plc
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Elliott, Nicole - Ichimoku Charts: An Introduction to Ichimoku Kinko Clouds - Harriman House Ltd.

Sperandeo, Victor - Trader Vic: Methods of a Wall Street Master - John Wiley & Sons Inc 1993
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Schwager, Jack D. - Market Wizards - New York Institute of Finance/Simon & Schuster 1989
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Schwager, Jack D. - The New Market Wizards - Harper-Collins 1992
附录2：历史上的学术论文
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1925 SCHULTZ, Henry, Forecasting Security Prices, Journal of the American Statistical Association, Volume 20, Issue 150 (Jun., 1925), 244-249.
1932 KING, Willford I., Forecasting Methods Successfully Used Since 1928, Journal of the American Statistical Association, Volume 27, Issue 179 (Sep., 1932), 315-319.
1933 COWLES, Alfred 3rd, Can Stock Market Forecasters Forecast?, Econometrica, Volume 1, Issue 3 (Jul., 1933), 309-324.
Cowles (1933) found that stock market forecasters cannot forecast.
1934 KING, Willford I., Technical Methods of Forecasting Stock Prices, Journal of the American Statistical Association, Volume 29, Issue 187 (Sep., 1934), 323-325.
1952 GORDON, R. A., Wesley Mitchell and the Study of Business Cycles, Journal of Business of the University of Chicago, Volume 25, Issue 2 (Apr., 1952), 101-107.
1955 ROOS, Charles F., Survey of Economic Forecasting Techniques: A Survey Article, Econometrica, Volume 23, Issue 4 (Oct., 1955), 363-395.
1959 ROBERTS, Harry V., Stock-Market "Patterns" and Financial Analysis: Methodological Suggestions, Journal of Finance, Volume 14, Issue 1 (Mar., 1959), 1-10.
Roberts (1959) suggests that, "There is every reason to believe, however, that this methos of looking at the tape will facilitate all that takes place afterward.“
1963 WEINTRAUB, Robert E., On Speculative Prices and Random Walks A Denial Journal of Finance, Volume 18, Issue 1 (Mar., 1963), 59-66.
1963 ZARNOWITZ, Victor, On the Dating of Business Cycles, Journal of Business, Volume 36, Issue 2 (Apr., 1963), 179-199.
1964 ALEXANDER, Sidney S., Price Movements in Speculative Markets: Trends or Random Walks, 1964.
Alexander (1964) concludes with, "The findings surveyed in this paper can be summarized by the statement that in speculative markets price changes appear to follow a random
walk over time, but a move, once initiated, tends to persist. In particular, if the stock markets has moved up x per cent it is likely to move up more than x per cent further before it
moves down by x per cent.“
1965 ATKINSON, Sue N., Financial Flows in Recent Business Cycles, Journal of Finance, Volume 20, Issue 1 (Mar., 1965), 14-35.
1965 FAMA, Eugene F., The Behavior of Stock-Market Prices, Journal of Business, Volume 38, Issue 1 (Jan., 1965), 34-105.
Fama (1965) concludes that, "chart reading, though perhaps an interesting pastime, is of no real value to the stock market investor.“
1966 FAMA, Eugene F. and Marshall E. BLUME, Filter Rules and Stock-Market Trading, Journal of Business, Volume 39, Issue 1, Part2: Supplement on Security Pricing (Jan.,
1966), 226-241.
1967 LEVY, Robert A., Relative Strength as a Criterion for Investment Selection, Journal of Finance, Volume 22, Issue 4 (Dec., 1967), 595-610.
Levy (1967) concludes with, "Although it appears that superior profits can be achieved by investing in securities which historically have been relatively strong in price movement,
the random walk hypothesis is not thereby refuted. To the extent that the superior profits are attributable to the incurrence of extraordinary risk.“
1968 HOWREY, E. Philip, A Spectrum Analysis of the Long-Swing Hypothesis, International Economic Review, Volume 9, Issue 2 (Jun., 1968), 228-252.
1968JAMES, F. E., Jr., Monthly Moving Averages--An Effective Investment Tool?, Journal of Financial and Quantitative Analysis, Volume 3, Issue 3, Special Issue: Random Walk
Hypothesis (Sep., 1968), 315-326.
1969 MELNIK, Arie and Alan KRAUS, Short-Run Interest Rate Cycles in the U.S.: 1954-1967, Journal of Financial Quantitative Analysis, Volume 4, Issue 3 (Sep., 1969), 291-299.
附录3：近期的学术论文
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ADLAND, R. and S. STRANDENES, 2006. Market efficiency in the bulk freight market revisited. Maritime Policy & Management.
AFOLABI, M.O. and O. OLUDE, 2007. Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM). Proceedings of the Fortieth Annual Hawaii International
ATMEH, M.A. and I.M. DOBBS, 2006. Technical analysis and the stochastic properties of the Jordanian stock market index return. Studies in Economics and Finance.
BABA, N., 2006. Utilization of NNs for Improving the Traditional Technical Analysis in the Financial Markets. LECTURE NOTES IN COMPUTER SCIENCE.
BAKER, A., 2006. American power and the dollar: The constraints of technical authority and declaratory policy in the …. New Political Economy.
BASK, M. and J. FIDRMUC, 2006. Fundamentals and Technical Trading: Behavior of Exchange Rates in the CEECs. Bank of Finland discussion paper.
BEHNCKE, H., 2006. Spectral analysis of fourth order differential operators II. Math. Nachr.
BELFORD, P., 2006. Candlestick stock analysis with genetic algorithms. Proceedings of the 8th annual conference.
CASSENS, J. and A. KOFOD-PETERSEN, 2006. Using Activity Theory to Model Context Awareness: a Qualitative Case Study. Proceedings of the 19th International Florida Artificial
CHANG, Y.H., M. METGHALCHI and C.C. CHAN, 2006. Technical trading strategies and cross-national information linkage: the case of Taiwan stock market. Applied Financial Economics.
CHEN, A. and P. BICKEL, 2006. Efficient independent component analysis. Annals of Statistics.
CHEUNG, Y.W., 2006. An Empirical Model of Daily Highs and Lows.
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