道德经炒股法（道德经在股市的智慧）

内容导航：

道，法，术，器，势在《道德经》中有原句解释么

炒股几个阶段 淘股吧

人法地，地法天，天法道，道法自然。。 这是道德经第几章说的？

易文斌：绝顶炒股高手都是怎么样炒股的

请教炒股之道

请问谁知道崔氏《股市道德经》的内容

怎么样才算真正的学会炒股

Q1：道，法，术，器，势在《道德经》中有原句解释么

只能说相对的

Q2：炒股几个阶段 淘股吧

闪牛分析：新手炒股的几个阶段:
第一阶段: 《投资基础知识》《投资的秘密》《精通K线图》《形态学大全》《投资心理学》《金融行为学》《大作手回忆录》《波浪理论》《趋势投资精要》《滚雪球》等;
第二阶段: 《莫生气》《金刚经》《般若波罗蜜多心经》《道德经》《庄子》《圣经》《黄帝内经》《思想政治》《论持久战》;
第三阶段: 《心脏病的预防与治愈》《高血压降压宝典》《抑郁症与狂躁症心理疗法》《强迫症的自我恢复》《精神病症状学》《药王神篇》《辨证施治》;
第四阶段: 《活着》
第五阶段: 《桥洞御寒指南》《如何试吃吃到饱》《五块钱如何花三天》

Q3：人法地，地法天，天法道，道法自然。。 这是道德经第几章说的？

I am totally layman and it is just for your reference. Vector AutoRegression (VAR) analysis This small guide shows you how to recreate the results for the VAR analysis used as an example in class. For the corresponding graphs and tables see the sheets/handouts on this topic. I used EViews4. The following steps are deliberately kept easy, using the EViews point-and-click approach. However, I assume you have familiarized yourself with the operation of EViews and understand the various routes one can take to obtain the results described below. Start EViews Select Menu options File/Open/Workfile Select the existing workfile FTA.wf1 from the directory where you saved it. The workfile contains the time series data for the FT-All Share price index, FT-All Share dividend index, yield on 20 year Gilts (government bonds), rate on 91 day Treasury bills. Monthly observations, January 1965 to December 1965 (although the interest rate time series go back much further). Additional series are created in the command window at the top as Smpl @all Series lprice = log(ftaprice) Series ldiv = log(ftadiv) Series dlprice = lprice – lprice(-1) or d(lprice) Series dldiv = ldiv – ldiv(-1) or d(ldiv) Series dr20 = r20 – r20(-1) or d(r20) Series drs = rs – rs(-1) or d(rs) 1. UNIT ROOT TESTS We have already discussed how to perform unit root tests in the time series example. You normally do these tests to decide on a VAR model in levels or first-differences of the variables. The VAR literature is special in the sense that some econometricians have argued that the debate on stationary, nonstationary variables is mostly irrelevant for VAR modelling and that one is allowed to use a levels VAR in any case. However, results sometimes are different for the two models. 2. VAR MODEL ESTIMATE First select the series you wish to analyze. Here I we look at the series dldiv, dlprice, dr20, drs Press CTRL and left-click the variables with your mouse. Select Workfile window Objects/New Object/Group Select Group window Procs/Make Vector Autoregression Select Unrestricted VAR, sample 1965:01 1995:12 (This is the full data period. However, the estimations will automatically adjust the sample period for missing observations!) Cointegration and VECM models will be discussed later. Lag length selection criteria Select VAR window View/Lag Structure/Lag length criteria, max lags 12 You will find the model selection criteria log-likelihood, AIC, SIC, HQ. Note that the estimation period of the VARs in this option is the same for all the models considered and therefore depends on the max. number of lags you want to consider. Once you have decided the optimal number of lags, the estimation period changes according to the selected lag number. Check for residual correlation. The traditional approach, using stationary variables in the VAR model, also recommends that residuals of the VAR equations do not exhibit residual correlation. (If you use the VAR in its long run, levels form as suggested by Sims and others, and do not worry about I(1) variables, the test on residual correlation is considered less important.) The lag length criteria suggest a model with either 1 or 2 lags. Select VAR window Estimate. Select Lag intervals 1 1 Select VAR window View/Residual Tests/Portmanteau Autocorrelation Test/ Lags 12 Repeat for VAR model with lag intervals 1 2 Applying the no-residual-correlation criterium, a VAR model is selected with 2 lags. View the final VAR estimation results by selecting VAR window Stats (note that the VAR model is already in its 2 lag form). Forecasts To work with the estimated VAR model in forecasts and simulations, you have to transfer the estimated equations into a model (a set of equations) to be used in EViews model operations. EViews allows you to make this model very easily. Select VAR window Procs/Make Model/Solve. Set solution sample to 1996:01 2000:12 You will see the results of the forecasting appear in the Workfile window because there are new variables being generated called <>_0 These variables contain the simulated or in this case forecasted values of the VAR variables. (Note that for the period before 1996:01, these variables still contain the original, actual values.) Produce the graphs Select Model window Procs/Make Graph and select the endogenous variables, series grouping as each series in its own graph, sample period 1995:01 1996:12, for the Baseline scenario (the variables referred to as _0). You can also select the variables _0 and make a new EViews Object/Group window. Residual correlation matrix Select VAR window View/Residual Tests/Correlograms. Select Tabulated by variable and lags 0. The correlation matrix is useful to determine whether the ordering in the recursive VAR method (Cholesky decomposition) is likely to influence results. Note that for the IRF and FEDV operations, the residuals of the VAR equations must be orthogonalized. Impulse response functions Select VAR window Impulse (or View/Impulse Response …). Under Impulse Definition select Cholesky – dof adjusted and variables Ordering dldiv dlprice dr20 drs. Under Impulse Display select Multiple Graphs, Response standard errors – Monte Carlo, and periods 10 (you can select more periods, but the results are not interesting in this case). Note that you can modify the order of the variables, which changes the identification scheme of the VAR shocks and affects the IRFs and FEDVs. EViews allows you to chose the format of the calculations as tabular or graphic (the latter is usually better for IRFs while tabular is often preferred for FEDVs). Note that there are alternative ways of producing standard errors for the IRFs. Of course, you need the st.errors to evaluate whether the estimated impulse response at a given horizon can be considered statistically significant. It is usual to select the Monte Carlo method and request at least a 1000 replications. Repeat making this graph by selecting Accumulated Responses under Impulse Display. This option is relevant here because the VAR model is using first-differences of the variables. So, if you want to new the developments in variables levels, you have to accumulate the first-differences. Forecast error decomposition of variances Select VAR window View/Variance Decomposition…. Select Multiple Graphs, Standard errors – None, Periods 10 If you want you can produce the output as tables rather than graphs. The use of the standard errors is ignored here, because we do not want to clutter the output. To evaluate statistical significance and confidence intervals you need the standard errors. Granger causality tests Select VAR window View/Lag Structure/Pairwise Granger causality tests Note that this option only provides results for the variables other than the dependent variable of the equation itself. This is inherent to the concept of Granger causality tests. Marginal significance tests. In a more general test of the VAR model we also investigate the significance of the own lags of the dependent variables. In EViews you would have to estimate all equations of the VAR separately (e.g. using OLS) and view the coefficient restrictions tests for these equations (Wald-test, F-test, log-likelihood-ratio test). Estimating these equations separately and doing the tests will provide slightly different results than the VAR system estimation (see the EViews guide on VAR estimation). In our example: Estimate the first VAR equation using OLS in EViews with the command LS dldiv c dldiv(-1 to -2) dlprice (-1 to -2) dr20(-1 to -2) drs(-1 to -2) Select Equation window View/Coefficient Tests/ Wald – Coefficient Restrictions, coefficient restrictions for lags of dldiv are c(2)=0,c(3)=0 (note that coefficient c(1) is the constant term or intercept of the equation). The Chi-square statistic is the Wald test. The F-statistic is for the equivalent F-test. Note: Coefficient restrictions allows you to set specific coefficients to specific values. In this case we set coefficients to zero, but it can be any value. Redundant Variables – Likelihood Ratio, test series is dldiv(-1 to -2). The log-likelihood ratio statistic is what we look for. The F-statistic is the equivalent F-test, in this case the same one as you find in the Wald-test option. Note: The redundant variables test is similar/equal to the coefficient restriction test for coefficient values set to zero, but in the redundant variables test the only coefficient value that can be tested is zero. F-test: see the results presented along with the Wald and LR tests. APPENDIX The following tests on VARs are not easily implemented in EViews using its standard options. These tests involve estimating the VAR equations individually and calculating some of the needed results using the EViews programming features. The tests are sometimes used in empirical studies (see Sims, 1980). Tests for dropping a variable from the entire VAR system. General tests of restrictions on the VAR model can be carried out by comparing the determinants of the restricted and unrestricted covariance matrices of the equation errors. The restrictions would be that the variable considered for exclusion has coefficients equal to zero in the equations for all the other variables (thus, except its own equation). The test statistic is LR = (T-k) [log(det(Restr)) – log(det(Unrestr))] Where T is the number of observations and k is the number of estimated coefficients in each equation of the unrestricted model (in our example, k = 4*2+1= 9). This statistic is distributed as 2 with degrees of freedom equal to the number of restrictions; in our example, 2 lags of a variable in (4-1=) 3 equations equal 6 restrictions. EViews presents you the value of the determinant of the residuals covariance matrix as part of the estimation output and also allows you to save the covariance matrix of residuals , and calculate its determinant. To apply the likelihood ratio test click on View/Residuals/Covariance matrix. Type command window matrix(n,n) rescov (with n the number of VAR equations) Select workfile window matrix variable rescov. Copy using Copy (Ctrl-C), Edit, Paste (Ctrl-V) the matrix values from the VAR window to the matrix window rescov. Type command window scalar det_cov1= @det (rescov) . If you select workfile variable det_cov1 you can see its value at the bottom line of the EViews window. (If you do this, compare your result with the value presented with the VAR output.) In EViews the Unrestricted VAR option does not allow you to impose the equation restrictions necessary for this test. But in principle you would repeat the previous actions for the restricted VAR model, calculate the likelihood ratio statistic, and determine its significance level from the 2 distribution. You can in EViews estimate the VAR models following the equation-by-equation approach, store the residuals for each equation, calculate the residuals covariance matrix, calculate the matrix determinant, and calculate the LR test statistic. If you write a small EViews program it is less work than you might think. Tests for structural change (i.e. comparing 2 subsample periods) Create an unrestricted VAR where in each equation additional variables enter as dummy variables (values 0,1 for the two subperiods) multiplied by the original variables values (including the constant term!). Also create the restricted VAR without the dummy*variable part in the equations. You can test the null hypothesis of no structural change equation-by-equation by testing the significance of all the dummy variables (Wald-test, F-test, log-likelihood-ratio test) in every single equation. You can test the null hypothesis of no structural change for the entire VAR model, by using the LR test based on the determinants of the residual covariance matrixes (see above). The LR statistic is distributed as 2 with degrees of freedom equal to the number of restrictions; in this case, dummies for 2 lags of each variable plus the intercept in 4 equations, equaling 4*(2*4+1) = 36 restrictions.
boardid=33751&id=51113

Q4：易文斌：绝顶炒股高手都是怎么样炒股的

闪牛分析：
很多人看过武侠小说中的“一阳指”和“降龙十八掌”等所谓“绝技”，并且他们也知道这是假的。因为人们大多知道真实的武打格斗是什么样子，比如拳击比赛或街头打架等。但是真正的操盘高手或者短线大师炒股是用什么方法或有什么炒股绝招，却极少有股民能够真正的知晓。湖北卫视天生易文斌财易文斌
真正炒股高手达到较高水平后就会像《道德经》所说的那样：明道若昧 进道若退 夷道若纇 意思就是：最明白的人反而会表现的像是很糊涂，最进步的人反而会显得像在后退，最容易的事情做起来反而像很困难。实际上这些说的就是易文斌们平时知道的那个成语——大智若愚
什么叫大智若愚呢？
就是站在了很高的角度上全局看问题而对细枝末节不加关注。由于在非根本问题上的模糊和缺少应变因而在普通人看来显得较为傻缺和呆板。但是由于他抓住了核心和根本，所以他总能取得最后的胜利。这就是大智慧。
就如三国时的司马懿与诸葛亮对战。尽管诸葛亮有千条妙计，机关莫测。但是司马懿守住交战要道，以守为进，以国家实力做为根本。看似方法蠢笨，但是却抓住了最大的根本原则，使有神鬼莫测能力的诸葛亮也无可奈何。
真正的交易之道就是如此。
明道若昧——真正的高手绝大多数时间都像是很糊涂，他们对未来经常表示看不清楚 很多股民肯定以为炒股高手一定能股市和个股走势进行精确的预测，并制定精确的买卖点。其实，根本就不是这样的！
就如很多世界射击高手都是近视眼一样，
真正的炒股高手都是对走势的判断较为模糊的。那些动辙预测未来几年经济走势和未来几年股市行情的人，在真正的炒股高眼中看来他们就是笑话。
炒股高手极少预测，就是偶尔做出预测自己也不是很当真。

Q5：请教炒股之道

成交量的放大说明交易活跃，
在低点放量可能有机构进入，
可以适量跟进。
在高点放量下跌可能有机构获利了结！~

Q6：请问谁知道崔氏《股市道德经》的内容

楼主直接百度一下就可以了，我刚刚试了一下，出来的内容不少，帖子什么的都有，楼主不妨去看看。
是不是民间高手这个就不知道了。

Q7：怎么样才算真正的学会炒股

不要与股市行情作对，不要为特定的需要去从事投机。
买进靠耐心，卖出靠决心，休息靠信心。
只要比别人多冷静一分，便能在股市中脱颖而出。
不要妄想在最低价买进，于最高价卖出。
股票买卖不要耽误在几个“申报价位”上。
市场充满乐观气氛，利多消息频传，股价大涨，连续上涨几十个涨停板，连冷门股都出现涨停板时应考虑卖出。
股民大众是盲从的，因此应在别人买进时卖出，在别人卖出时买进。
放长线钩大鱼，好酒放得愈久愈香。
以投资的眼光计算股票，以投机的技巧保障利益。
买股票如学游泳，不在江河之中沉浮几次，什么也学不会。
天天都去股市的人，不比市场外的投资者赚钱。
专家不如炒家，炒家不如藏家。
股市无常胜将军。
赚到手就存起来，等于把利润的一半锁进保险箱。
分次买，不赔钱；一次买，多赔钱。
在行情跳空开盘时应立即买进或卖出。
许多股民时常随市场大势抢出抢进，没有自己的投资主张，而造成无谓的损失。
初入股市的新手，最好从事长期投资，并选择税后利润高，流通性好的热门股票。
"剪成数段再接起来的绳子，再接起来一定比原来的短。"买卖股票，短线操作者最后肯定不如长期投资者的人获利得多！
不准备做委托买卖时，最好远离市场，天天到证券公司观望行情的人，容易受行情变化及市场的渲染而作出错误的决策。
胆量大，心思细，决心快，是成功的三项条件。
股票新手不要急于入市，可以去游侠股市，通过模拟炒股先了解下基本东西，对入门学习、锻炼实战技巧很有帮助。
上升行情中遇到小跌要买，下跌行情中遇到小涨要卖。
行情涨了一段时期后，成交量突然破记录，暴增或逐渐萎缩时，大概就是最高峰了！卖出时动作要快，买进时不妨多斟酌。
如果错了一次买进的良机，就把它忘记，股市上的机会无穷无尽，只要你有足够的耐心且保持镇定，你总能抓住一两次大行情。
投入股票的金额，不要超过可以承受损失的能力。尤其是对全额交割，更应特别小心。
以上涨三成作为卖出目标，这是制订投资目标的基准，也是买卖股票方法之一。
放不过机遇，就躲不过风险。
股票没有好坏之分，买股票就怕炒来炒去，见异思迁，心猿意马。
买股票虽然不容易，卖股票也是一门大学问，许多股民很会买股票，却不懂得如何卖股票。事实上，一个真正成功的股民，是懂得在最适当的时机卖出高价。
最大价下跌，或量大价不跌，如出现在股价大的涨幅之后，应断然出局以保战果，须知股价上涨必须有增量的配合。
每个已入市的股民，都应该制作一张买卖股票的记录卡，亲自记录自己的买卖操作，可以加深失败的教训，这样才能避开历史的重演。避免重蹈覆辙。
什么时候买比买什么更重要，选择买的时机比选择买什么股票更重要。
买进股票之前，先写下五条支持你投资这家公司的理由，并随时检查，如果发现其中有三条理由已不存在，就应立刻卖出股票。
遇到亏损时应立刻了结，遇到赚钱时不要急于出手，但也不可贪图到最后的最高价位。
投资股票千万不要追价买卖。
看大方向赚大钱，看小方向赚小钱。
买卖股票是为了盈利，但要学会将盈亏置之度外。
股市由低谷反弹时，前三天仍为不稳定期，要看以后一周的走势，才是决定股市是否远离谷底的关键时刻。
唯有休息才能保障即得之利益，唯有休息才能养足精神，争取下一回合的胜利。
忙于工作的股民，不妨选择定量定时投资法。
可由"买少量、买多样"来体验股票赚钱之道。
市场往东，你最好不要往西，喜欢和市场做对的人没有好下场。
不在大涨之后买进，不在大跌之后卖出。
黑马股可遇不可求，投资胜票仍应以踏实为主。
不要因为一个升降单位而贻误时机。
申购新股票要慎重选择，股民吃亏上当的事已屡见不鲜。
投资人，为成功的投机；而投机人，乃失败的投资。
若要在不安定中寻找安定，买进股票最好不要超过3～5种。
买进一流大公司的股票，乃是正确的，但应注意其未来的发展性。
会做股票的人，一年只做少数几次就够了；赚了钱而舍不得离开的人，终究会亏了老本。
股市里买进机会多，卖出机会少。
对投资者而言，能利用较短的中期趋势，要比做长期趋势所得更多。
不在成交大增之后买进，不在成交量大减之后卖出。