Proposed Short of $ODFL Finished on LowsFinished on lows of the day. I propose the idea of going short this overbought trucking stock. All ideas are opinions only and should not be considered trading advice.
ODFL
Annstrategy
GBP/USD Short (MACD, ADX signal)The GBP/USD has been fairly bullish recently. With the USD weak last week it gave the neutral GBP some power (as shown) With the USD going back neutral the pair should drop. This is shown both by the ANN strategy and MACD. The ADX is also low indicating a big movement. The resisitance line would be a good time to enter the trade and T1 shows some more resistance. Take out 1/3 of your contracts then so you can afford to lose some if it bounces back up. If T1 is broken there is not much resistance and it should drop quite a bit. Also look for the EMA's to cross. If they do it is more signal to short
ANN Strategy Indicator 3.2 (Rebuilt By Kevin Manrrique)So I rebuilt the ANN Strategy with my own codes I added. It took me a long time to get this far, the first 2 weeks it wasn't repainting but then it started repainting slowly. Not hard like the original script. Please feel free to edit the script and post it at the bottom. Thank you everyone! I hope this script I rebuilt can help people! That's why were all here for, a community!
This script is meant to be use in hourly time frames!
If possible use with USD pairs.
Works great with news also!
//@version=2
study("ANN Strategy Indicator 3.2 (Rebuilt By Kevin Manrrique)", overlay=false, precision=777)
threshold = input(title="Threshold", type=float, defval=0.001, step=0.001)
largeTimeframe = input(title="Large timeframe", type=resolution, defval='D')
smallTimeframe = input(title="Small timeframe", type=resolution, defval='60')
PineActivationFunctionLinear(v) => v
PineActivationFunctionTanh(v) =>
(exp(v) - exp(-v))/(exp(v) + exp(-v))
ANN(input) =>
l0_0 = PineActivationFunctionLinear(input)
l0_1 = PineActivationFunctionLinear(input)
l0_2 = PineActivationFunctionLinear(input)
l0_3 = PineActivationFunctionLinear(input)
l0_4 = PineActivationFunctionLinear(input)
l0_5 = PineActivationFunctionLinear(input)
l0_6 = PineActivationFunctionLinear(input)
l0_7 = PineActivationFunctionLinear(input)
l0_8 = PineActivationFunctionLinear(input)
l0_9 = PineActivationFunctionLinear(input)
l0_10 = PineActivationFunctionLinear(input)
l0_11 = PineActivationFunctionLinear(input)
l0_12 = PineActivationFunctionLinear(input)
l0_13 = PineActivationFunctionLinear(input)
l0_14 = PineActivationFunctionLinear(input)
l1_0 = PineActivationFunctionTanh(l0_0*5.040340774 + l0_1*-1.3025994088 + l0_2*19.4225543981 + l0_3*1.1796960423 + l0_4*2.4299395823 + l0_5*3.159003445 + l0_6*4.6844527551 + l0_7*-6.1079267196 + l0_8*-2.4952869198 + l0_9*-4.0966081154 + l0_10*-2.2432843111 + l0_11*-0.6105764807 + l0_12*-0.0775684605 + l0_13*-0.7984753138 + l0_14*3.4495907342)
l1_1 = PineActivationFunctionTanh(l0_0*5.9559031982 + l0_1*-3.1781960056 + l0_2*-1.6337491061 + l0_3*-4.3623166512 + l0_4*0.9061990402 + l0_5*-0.731285093 + l0_6*-6.2500232251 + l0_7*0.1356087758 + l0_8*-0.8570572885 + l0_9*-4.0161353298 + l0_10*1.5095552083 + l0_11*1.324789197 + l0_12*-0.1011973878 + l0_13*-2.3642090162 + l0_14*-0.7160862442)
l1_2 = PineActivationFunctionTanh(l0_0*4.4350881378 + l0_1*-2.8956461034 + l0_2*1.4199762607 + l0_3*-0.6436844261 + l0_4*1.1124274281 + l0_5*-4.0976954985 + l0_6*2.9317456342 + l0_7*0.0798318393 + l0_8*-5.5718144311 + l0_9*-0.6623352208 +l0_10*3.2405203222 + l0_11*-10.6253384513 + l0_12*4.7132919253 + l0_13*-5.7378151597 + l0_14*0.3164836695)
l1_3 = PineActivationFunctionTanh(l0_0*-6.1194605467 + l0_1*7.7935605604 + l0_2*-0.7587522153 + l0_3*9.8382495905 + l0_4*0.3274314734 + l0_5*1.8424796541 + l0_6*-1.2256355427 + l0_7*-1.5968600758 + l0_8*1.9937700922 + l0_9*5.0417809111 + l0_10*-1.9369944654 + l0_11*6.1013201778 + l0_12*1.5832910747 + l0_13*-2.148403244 + l0_14*1.5449437366)
l1_4 = PineActivationFunctionTanh(l0_0*3.5700040028 + l0_1*-4.4755892733 + l0_2*0.1526702072 + l0_3*-0.3553664401 + l0_4*-2.3777962662 + l0_5*-1.8098849587 + l0_6*-3.5198449134 + l0_7*-0.4369370497 + l0_8*2.3350169623 + l0_9*1.9328960346 + l0_10*1.1824141812 + l0_11*3.0565148049 + l0_12*-9.3253401534 + l0_13*1.6778555498 + l0_14*-3.045794332)
l1_5 = PineActivationFunctionTanh(l0_0*3.6784907623 + l0_1*1.1623683715 + l0_2*7.1366362145 + l0_3*-5.6756546585 + l0_4*12.7019884334 + l0_5*-1.2347823331 + l0_6*2.3656619827 + l0_7*-8.7191778213 + l0_8*-13.8089238753 + l0_9*5.4335943836 + l0_10*-8.1441181338 + l0_11*-10.5688113287 + l0_12*6.3964140758 + l0_13*-8.9714236223 + l0_14*-34.0255456929)
l1_6 = PineActivationFunctionTanh(l0_0*-0.4344517548 + l0_1*-3.8262167437 + l0_2*-0.2051098003 + l0_3*0.6844201221 + l0_4*1.1615893422 + l0_5*-0.404465314 + l0_6*-0.1465747632 + l0_7*-0.006282458 + l0_8*0.1585655487
