Can you please provide the formula for Moving Linear Regression for the 15, 75, 150 and 250 periods. for use in a PCF.

It seems that  the built in indicators for moving linear regression do not work with alerts or column conditions for MLR crossovers but they do work if a pcf is created using the formulas. I looked through the forum and copied some of the periods I need but did not see the formulas for the specific periods above.  Thanks

15-Period Linear Regression:

AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280

75-Period Linear Regression:

AVGC75 + 37 * (37 * C + 36 * C1 + 35 * C2 + 34 * C3 + 33 * C4 + 32 * C5 + 31 * C6 + 30 * C7 + 29 * C8 + 28 * C9 + 27 * C10 + 26 * C11 + 25 * C12 + 24 * C13 + 23 * C14 + 22 * C15 + 21 * C16 + 20 * C17 + 19 * C18 + 18 * C19 + 17 * C20 + 16 * C21 + 15 * C22 + 14 * C23 + 13 * C24 + 12 * C25 + 11 * C26 + 10 * C27 + 9 * C28 + 8 * C29 + 7 * C30 + 6 * C31 + 5 * C32 + 4 * C33 + 3 * C34 + 2 * C35 + C36 - C38 - 2 * C39 - 3 * C40 - 4 * C41 - 5 * C42 - 6 * C43 - 7 * C44 - 8 * C45 - 9 * C46 - 10 * C47 - 11 * C48 - 12 * C49 - 13 * C50 - 14 * C51 - 15 * C52 - 16 * C53 - 17 * C54 - 18 * C55 - 19 * C56 - 20 * C57 - 21 * C58 - 22 * C59 - 23 * C60 - 24 * C61 - 25 * C62 - 26 * C63 - 27 * C64 - 28 * C65 - 29 * C66 - 30 * C67 - 31 * C68 - 32 * C69 - 33 * C70 - 34 * C71 - 35 * C72 - 36 * C73 - 37 * C74) / 35150

150-Period Linear Regression:

AVGC150 + 74.5 * (74.5 * C + 73.5 * C1 + 72.5 * C2 + 71.5 * C3 + 70.5 * C4 + 69.5 * C5 + 68.5 * C6 + 67.5 * C7 + 66.5 * C8 + 65.5 * C9 + 64.5 * C10 + 63.5 * C11 + 62.5 * C12 + 61.5 * C13 + 60.5 * C14 + 59.5 * C15 + 58.5 * C16 + 57.5 * C17 + 56.5 * C18 + 55.5 * C19 + 54.5 * C20 + 53.5 * C21 + 52.5 * C22 + 51.5 * C23 + 50.5 * C24 + 49.5 * C25 + 48.5 * C26 + 47.5 * C27 + 46.5 * C28 + 45.5 * C29 + 44.5 * C30 + 43.5 * C31 + 42.5 * C32 + 41.5 * C33 + 40.5 * C34 + 39.5 * C35 + 38.5 * C36 + 37.5 * C37 + 36.5 * C38 + 35.5 * C39 + 34.5 * C40 + 33.5 * C41 + 32.5 * C42 + 31.5 * C43 + 30.5 * C44 + 29.5 * C45 + 28.5 * C46 + 27.5 * C47 + 26.5 * C48 + 25.5 * C49 + 24.5 * C50 + 23.5 * C51 + 22.5 * C52 + 21.5 * C53 + 20.5 * C54 + 19.5 * C55 + 18.5 * C56 + 17.5 * C57 + 16.5 * C58 + 15.5 * C59 + 14.5 * C60 + 13.5 * C61 + 12.5 * C62 + 11.5 * C63 + 10.5 * C64 + 9.5 * C65 + 8.5 * C66 + 7.5 * C67 + 6.5 * C68 + 5.5 * C69 + 4.5 * C70 + 3.5 * C71 + 2.5 * C72 + 1.5 * C73 + 0.5 * C74 - 0.5 * C75 - 1.5 * C76 - 2.5 * C77 - 3.5 * C78 - 4.5 * C79 - 5.5 * C80 - 6.5 * C81 - 7.5 * C82 - 8.5 * C83 - 9.5 * C84 - 10.5 * C85 - 11.5 * C86 - 12.5 * C87 - 13.5 * C88 - 14.5 * C89 - 15.5 * C90 - 16.5 * C91 - 17.5 * C92 - 18.5 * C93 - 19.5 * C94 - 20.5 * C95 - 21.5 * C96 - 22.5 * C97 - 23.5 * C98 - 24.5 * C99 - 25.5 * C100 - 26.5 * C101 - 27.5 * C102 - 28.5 * C103 - 29.5 * C104 - 30.5 * C105 - 31.5 * C106 - 32.5 * C107 - 33.5 * C108 - 34.5 * C109 - 35.5 * C110 - 36.5 * C111 - 37.5 * C112 - 38.5 * C113 - 39.5 * C114 - 40.5 * C115 - 41.5 * C116 - 42.5 * C117 - 43.5 * C118 - 44.5 * C119 - 45.5 * C120 - 46.5 * C121 - 47.5 * C122 - 48.5 * C123 - 49.5 * C124 - 50.5 * C125 - 51.5 * C126 - 52.5 * C127 - 53.5 * C128 - 54.5 * C129 - 55.5 * C130 - 56.5 * C131 - 57.5 * C132 - 58.5 * C133 - 59.5 * C134 - 60.5 * C135 - 61.5 * C136 - 62.5 * C137 - 63.5 * C138 - 64.5 * C139 - 65.5 * C140 - 66.5 * C141 - 67.5 * C142 - 68.5 * C143 - 69.5 * C144 - 70.5 * C145 - 71.5 * C146 - 72.5 * C147 - 73.5 * C148 - 74.5 * C149) / 281237.5

250-Period Linear Regression:

AVGC250 + 124.5 * (124.5 * C + 123.5 * C1 + 122.5 * C2 + 121.5 * C3 + 120.5 * C4 + 119.5 * C5 + 118.5 * C6 + 117.5 * C7 + 116.5 * C8 + 115.5 * C9 + 114.5 * C10 + 113.5 * C11 + 112.5 * C12 + 111.5 * C13 + 110.5 * C14 + 109.5 * C15 + 108.5 * C16 + 107.5 * C17 + 106.5 * C18 + 105.5 * C19 + 104.5 * C20 + 103.5 * C21 + 102.5 * C22 + 101.5 * C23 + 100.5 * C24 + 99.5 * C25 + 98.5 * C26 + 97.5 * C27 + 96.5 * C28 + 95.5 * C29 + 94.5 * C30 + 93.5 * C31 + 92.5 * C32 + 91.5 * C33 + 90.5 * C34 + 89.5 * C35 + 88.5 * C36 + 87.5 * C37 + 86.5 * C38 + 85.5 * C39 + 84.5 * C40 + 83.5 * C41 + 82.5 * C42 + 81.5 * C43 + 80.5 * C44 + 79.5 * C45 + 78.5 * C46 + 77.5 * C47 + 76.5 * C48 + 75.5 * C49 + 74.5 * C50 + 73.5 * C51 + 72.5 * C52 + 71.5 * C53 + 70.5 * C54 + 69.5 * C55 + 68.5 * C56 + 67.5 * C57 + 66.5 * C58 + 65.5 * C59 + 64.5 * C60 + 63.5 * C61 + 62.5 * C62 + 61.5 * C63 + 60.5 * C64 + 59.5 * C65 + 58.5 * C66 + 57.5 * C67 + 56.5 * C68 + 55.5 * C69 + 54.5 * C70 + 53.5 * C71 + 52.5 * C72 + 51.5 * C73 + 50.5 * C74 + 49.5 * C75 + 48.5 * C76 + 47.5 * C77 + 46.5 * C78 + 45.5 * C79 + 44.5 * C80 + 43.5 * C81 + 42.5 * C82 + 41.5 * C83 + 40.5 * C84 + 39.5 * C85 + 38.5 * C86 + 37.5 * C87 + 36.5 * C88 + 35.5 * C89 + 34.5 * C90 + 33.5 * C91 + 32.5 * C92 + 31.5 * C93 + 30.5 * C94 + 29.5 * C95 + 28.5 * C96 + 27.5 * C97 + 26.5 * C98 + 25.5 * C99 + 24.5 * C100 + 23.5 * C101 + 22.5 * C102 + 21.5 * C103 + 20.5 * C104 + 19.5 * C105 + 18.5 * C106 + 17.5 * C107 + 16.5 * C108 + 15.5 * C109 + 14.5 * C110 + 13.5 * C111 + 12.5 * C112 + 11.5 * C113 + 10.5 * C114 + 9.5 * C115 + 8.5 * C116 + 7.5 * C117 + 6.5 * C118 + 5.5 * C119 + 4.5 * C120 + 3.5 * C121 + 2.5 * C122 + 1.5 * C123 + 0.5 * C124 - 0.5 * C125 - 1.5 * C126 - 2.5 * C127 - 3.5 * C128 - 4.5 * C129 - 5.5 * C130 - 6.5 * C131 - 7.5 * C132 - 8.5 * C133 - 9.5 * C134 - 10.5 * C135 - 11.5 * C136 - 12.5 * C137 - 13.5 * C138 - 14.5 * C139 - 15.5 * C140 - 16.5 * C141 - 17.5 * C142 - 18.5 * C143 - 19.5 * C144 - 20.5 * C145 - 21.5 * C146 - 22.5 * C147 - 23.5 * C148 - 24.5 * C149 - 25.5 * C150 - 26.5 * C151 - 27.5 * C152 - 28.5 * C153 - 29.5 * C154 - 30.5 * C155 - 31.5 * C156 - 32.5 * C157 - 33.5 * C158 - 34.5 * C159 - 35.5 * C160 - 36.5 * C161 - 37.5 * C162 - 38.5 * C163 - 39.5 * C164 - 40.5 * C165 - 41.5 * C166 - 42.5 * C167 - 43.5 * C168 - 44.5 * C169 - 45.5 * C170 - 46.5 * C171 - 47.5 * C172 - 48.5 * C173 - 49.5 * C174 - 50.5 * C175 - 51.5 * C176 - 52.5 * C177 - 53.5 * C178 - 54.5 * C179 - 55.5 * C180 - 56.5 * C181 - 57.5 * C182 - 58.5 * C183 - 59.5 * C184 - 60.5 * C185 - 61.5 * C186 - 62.5 * C187 - 63.5 * C188 - 64.5 * C189 - 65.5 * C190 - 66.5 * C191 - 67.5 * C192 - 68.5 * C193 - 69.5 * C194 - 70.5 * C195 - 71.5 * C196 - 72.5 * C197 - 73.5 * C198 - 74.5 * C199 - 75.5 * C200 - 76.5 * C201 - 77.5 * C202 - 78.5 * C203 - 79.5 * C204 - 80.5 * C205 - 81.5 * C206 - 82.5 * C207 - 83.5 * C208 - 84.5 * C209 - 85.5 * C210 - 86.5 * C211 - 87.5 * C212 - 88.5 * C213 - 89.5 * C214 - 90.5 * C215 - 91.5 * C216 - 92.5 * C217 - 93.5 * C218 - 94.5 * C219 - 95.5 * C220 - 96.5 * C221 - 97.5 * C222 - 98.5 * C223 - 99.5 * C224 - 100.5 * C225 - 101.5 * C226 - 102.5 * C227 - 103.5 * C228 - 104.5 * C229 - 105.5 * C230 - 106.5 * C231 - 107.5 * C232 - 108.5 * C233 - 109.5 * C234 - 110.5 * C235 - 111.5 * C236 - 112.5 * C237 - 113.5 * C238 - 114.5 * C239 - 115.5 * C240 - 116.5 * C241 - 117.5 * C242 - 118.5 * C243 - 119.5 * C244 - 120.5 * C245 - 121.5 * C246 - 122.5 * C247 - 123.5 * C248 - 124.5 * C249) / 1302062.5

Using Linear Regression vs Classical Peaks/Valleys for Divergence Analysis

Thank You Bruce

Bruce,

Could you also include a Moving Linear Regression 12 PCF please?

12-Period Linear Regression:

AVGC12 + 5.5 * (5.5 * C + 4.5 * C1 + 3.5 * C2 + 2.5 * C3 + 1.5 * C4 + .5 * C5 - .5 * C6 - 1.5 * C7 - 2.5 * C8 - 3.5 * C9 - 4.5 * C10 - 5.5 * C11) / 143

Bruce,

Please help with a PCF that calcuates the % distance that Moving Linear Regression 15 is away from the current price.   

= (MLR15 - Price)/MLR15)*100  ????

= (AVGC15 + 7 * (7 * C ...C14) / 280  - ???)   /  (AVGC15 + 7 * (7 * C ...C14) / 280) *100     ???

And if possible a 2nd question:

A scan PCF condition which shows that MLR 15 went from moving down (or sideways) to up          (& vice-versa for moving down  - just substitute > for <   ???).    =MLR15 > MLR15.1 AND MLR15.1 < MLR15.2    ???

AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280
>
(AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280) .1
AND
(AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280) .1
<
(AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280) .2

Your technique for calculating the % distance would work, but can be simplified a bit so we only have to include the MLR15 formula once instead of twice:

100 * (C / (AVGC15 + 7 * (7 * C + 6 * C1 + 5 * C2 + 4 * C3 + 3 * C4 + 2 * C5 + C6 - C8 - 2 * C9 - 3 * C10 - 4 * C11 - 5 * C12 - 6 * C13 - 7 * C14) / 280) - 1)

You would have to adjust the bars ago parameters on the formulas for 1 bar ago and 2 bars ago in the last 3 linear regression formulas to make your technique work. If you do so however, something rather nice happens. There are 14 shared terms between the two MLR regression calculations of bars which are next to each other. This means your formula can be shortened significantly to:

29 * C + 13 * C15 > 42 * AVGC14.1 AND 29 * C1 + 13 * C16 <= 42 * AVGC14.2

Bruce,

How do you do it!?  - Really phenomenal & much appreciated. 

If I wanted the 2nd formula to show MLR 15 from moving up (or sideways) to down, would the formula be:

29 * C + 13 * C15 < 42 * AVGC14.1 AND 29 * C1 + 13 * C16 >= 42 * AVGC14.2

It seems to fit the ticket but I want to verify. 

Your formula should work nicely. I create the formulas through a combination of relatively simple algebra and spreadsheets.

Hi Bruce,

Please help w/ MLR 10 crossing MLR 30 (either up or down).

and also,

MLR 15 crossing MLR 30 (either up or down).  Thank you in advance! 

SGN(1118 * C + 872 * C1 + 626 * C2 + 380 * C3 + 134 * C4 - 112 * C5 - 358 * C6 - 604 * C7 - 850 * C8 - 1096 * C9 - 319 * C10 - 286 * C11 - 253 * C12 - 220 * C13 - 187 * C14 - 154 * C15 - 121 * C16 - 88 * C17 - 55 * C18 - 22 * C19 + 11 * C20 + 44 * C21 + 77 * C22 + 110 * C23 + 143 * C24 + 176 * C25 + 209 * C26 + 242 * C27 + 275 * C28 + 308 * C29) <> SGN(1118 * C1 + 872 * C2 + 626 * C3 + 380 * C4 + 134 * C5 - 112 * C6 - 358 * C7 - 604 * C8 - 850 * C9 - 1096 * C10 - 319 * C11 - 286 * C12 - 253 * C13 - 220 * C14 - 187 * C15 - 154 * C16 - 121 * C17 - 88 * C18 - 55 * C19 - 22 * C20 + 11 * C21 + 44 * C22 + 77 * C23 + 110 * C24 + 143 * C25 + 176 * C26 + 209 * C27 + 242 * C28 + 275 * C29 + 308 * C30)

SGN(427 * C + 358 * C1 + 289 * C2 + 220 * C3 + 151 * C4 + 82 * C5 + 13 * C6 - 56 * C7 - 125 * C8 - 194 * C9 - 263 * C10 - 332 * C11 - 401 * C12 - 470 * C13 - 539 * C14 - 112 * C15 - 88 * C16 - 64 * C17 - 40 * C18 - 16 * C19 + 8 * C20 + 32 * C21 + 56 * C22 + 80 * C23 + 104 * C24 + 128 * C25 + 152 * C26 + 176 * C27 + 200 * C28 + 224 * C29) <> SGN(427 * C1 + 358 * C2 + 289 * C3 + 220 * C4 + 151 * C5 + 82 * C6 + 13 * C7 - 56 * C8 - 125 * C9 - 194 * C10 - 263 * C11 - 332 * C12 - 401 * C13 - 470 * C14 - 539 * C15 - 112 * C16 - 88 * C17 - 64 * C18 - 40 * C19 - 16 * C20 + 8 * C21 + 32 * C22 + 56 * C23 + 80 * C24 + 104 * C25 + 128 * C26 + 152 * C27 + 176 * C28 + 200 * C29 + 224 * C30)