back start next
[start] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [ 70 ] [71]
70 Bibliography 219 Milne, William E. Numerical Calculus. Princeton, N. J.: Princeton University Press, 1949. Hamming, R. W. Numerical Methods for Scientists and Engineers. New York: McGraw-Hill Book Company, 1962. HI. TECHNICAL ANALYSIS OF THE STOCK MARKET Jiler, W. L. How Charts Can Help You in the Stock Market. New York: Trendline, Inc., 1962. Markstein, D. How to Chart Your Way to Stock Market Profits. Englewood Cliffs Prentice-Hall, Inc., 1967. Edwards, R. D., and J. Magee. Technical Analysis of Stock Trends. 5th ed. Springfield John Magee, Inc., n. d. . Cootner, Paul H., ed. The Random Character of Stock Market Prices. Cambridge M.I.T. Press, n. d.
Index Alloys UnBmited moving average, applications inverse, oenteied; 109-112 10 and 20 week; 63 10 week, centered; 65-66 trading interval effect; 23-25 Amount of move e<Sction; see Prediction Amplitude definition; 169 determination diptal flJtei; 44,45,46,193 Fourier analysis; 173-175 inverse moving averages: 109-112 Amplitude-deration, relationship; see Foailei analySs, Frequency, Proportionality ArapHtude-frequency, relationship; see Fotiifer analysis, Fiequency Angular frequency; see Frequency Brokerage displays; see Tracking of prices Buy; see Signals Capitalization; see Screening factors Giannels; see Envelope analysis Oiart patterns; see also Trendlines, Triangles box, coU, diamond, flag, pennant, wedge; 59,67 diannels; 52-53, 55-56, see also Envelope analysis double tops and bottoms; 57-5 S, 67 failure detection; 592 gaps, islands, measured moves, saucers, one-day reversals; 61 head and shoulders; 52, 54-56,60,67 validity concept; 61-62 "V" top and bottom; 61-67 Qiart services; see Services Coarse ftequency structure; see Fourier analysis. Filter Comb filters; see Filter Commonality examples; 44-49,201-203 expression; 32, 34-35,48, 201-203 principle; 28.23-33,34-35 time-synchronization; 32-33, 35, 48-49, 95,161, 166-167, 201-203 variations from; 32-33, 35, 201-203 Component durations tables of; see Nominality Component rate-change; see Proportionality Comporaiding; sec Profit Construction, envelopes; see Envelope analysis Contracted data; see Envelope analysis Cosjnusoidal amplitude determination; see Fourier analysis / Cover short; se/ Signals Curve fitting parabolic; see Interpolation, paraboUc straight line; 184-185 trigonometric; 185,215-217 usage; 183. 185 Cut-loss; see Signals CycKcality, state-analysis; 59-61, 73-76, 83-84, 90-91, 151-156 CycUc model; see Model Data services; see Sennces Decision influence; 30.35,141-145. 157 Digital filters; see Filters Dominance, of components; see Variation Duration; see Frequency, period relationship DUTation-ampBtude relationship; see ProportionaBty Duration-magnitude fluctuation; see Variation Duraticms, components, tables of; see Nominality Emotional cyclicality; see Pitfalls Envelope analysis application methods; 70-71,73-77,79-82,85, 87-88, 89-90.108.112-113, 124-125.128-130 centeiSne significance; 38,108 construction techniques; 37, 69-70, 85 contracted data; 40,41-42.45-48 examples; 25, 37-44.46-48.52, 65-66, 73-75, 79-83. 87-88, 124-126, 128-130, 133 expanded data; 40 nesting downward; 39-40 nesting upward; 38-39 non-iealtime; 91-93, 96, 124 Enor, filter; see Filter Expanded data; see Envelope analysis Failure of chart patterns; see Chart patterns Filter application; 182-183 combs; 188,191-196 definition; 175-176 design; 178-182 criteria; 179-180 frequency response; see also Frequency response, 177,179, 192 lag time; 176-177 moving average; see Moving average output;44-46. 151-152, 183,193. 196-197 weights; 177-178 Fine frequency structure; see Fourier analysis, Filter
Forseeable fundamentals; see Fundamentals Fouiiet analysis example, DJIA; 175,188-191 impfications ampHtiide-firequency relationship; 191 course frequency stnictuie; 190 fine frequency structure; 190 general; 175-192 Une spectrum, endence of; 189-191, 196-198 methcKi compoate amplitude detenninatioii; 175 cousoidal amplitude determination; 173-175 data assembly; 171 frequeiKy determination; 172-173 sequence separation; 172,173-174 sinusoidal amplitude determinatioo; 174 Frequency amplitude relationship course, spectral; 190 angular; 170 deOnition; 170 Hne nature; 189-190, 194-200 modulation; 33, 35, 37.44-46. 57-60,73-75, 92, 128-129.152, 157 period relatjonship; see also Rroportionality, 170 resolutjon course; 189-190 flne; 189-190 spectral; 189-190 response; see Frequency lesponss wave; 169-170 ctrura; 169-171,188-190 Frequency-atnpHlode relationship; see Fourier analysis. Frequency Frequency response centered moving average; 207-210 definition; 177 diptaJ filter; 175-177, 179-181 inverse moving average; 210-211 Frequency structure, stock prices; see Fourier analysis, FUters Full-span moving averages; Moving averas Fundamentals foreseeable, effect; 29. 31, 34, 48. 142,145-151,157, 159-162 gross national product; 149-151,157 histoncal events; see Historical events motivational; 29, 142-145 risk; 22, 142 timingaid; 29,145-151, 149-151,159-262 nnforseeable, effect; 31, 34,157 Geiergraph chart service; 116 Greed; see Pitfalls Gross national product; see Fundamentals Group prediction: see Tradmg experiment Gruen Industries; 71-84, 87-94 Half-span moving average; see Moving average. Signals Historical events; 30-31, 34, 48,145-154 Hold; see Signals Industry group prediction; see Trading experiment Interpolation, parabolic method; 212-214 usage; 185.212 Itiverse moving average; see Moving average, gnats Investing vs trading; see Profit lnational dec¹cm processes; see Decision ISL daily stock price index; 121 Issue sekction scan; see also Scan criteiU, 115-116,119-120.127 soreening; see also Soeening factors, 116-119,127 stable;3ee also Stable concept, US. 120-121 traditional; 115 Lag time; see Fflter, Moving avenge Least-square-ertor generalized method; 21S-217 paiaboHc data fit;2I2-2l4 straight data fit; 183 -185 trigonometric data flt; 215-217 usage; 183-186 Line Spectrmn; see Fourier analysis Magnltude-durationhectuation; see Variation Mansfield chart service; 186 Market potential; see Profit Maximization of jsofit; see Profit Mid-band; see Si*ls Model cycUc sub-model;31-35 , 38, 40.41, 44-*5,48,50, 52, 55,59. 61, 124-125, 128, 157 motivatjonal; 143-144,157 pricennotion; 10, 29. 30-34,50,55.57-59,115, 9-120,124-125,128,157,159-160 spectral; 199-200 Modulation; see Frequency Motivational model; see Model Moving average characteristics frequency response; 97-98, 176-178, 207-210 lag; 65-66. 67, 98. 112, 176, 208 smoothing; 63-65,67, 97-98.208-209 span; 63.67. 98, 208 weights; 178 examples; 63-66, 98-107.125-130,132-134 significance; 62-63, 62-65, 67, 208-210 types centered; 65-67 . 207-210 fuU-span; 98-108,112-113.124 half-span, 97-107. 112-113,124, 125-127, 132-134 inverse; 109-113,128-130,176,210-2U non-centeied; 63 Nested lows; see Signals Nesting; see Envelope analysis. Signals Nominality durations; table of; 33 principle; 33, 35, 50, 128, 199 Non-real-time envelope; see Envelope analysis Numeriral fflteis; see Filter Outside influences; see Pitfalls Period; see Frequency Persimmon effect; see Pitfalls Phase; 57, 169, 177, 207-211, 217 Ktfalls; emotional cyclicality, 166-167 gieed; 162-163 magnitude-duration fluctuation; 159
[start] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [ 70 ] [71]
|
Menu
