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You folks helped me with another part of this script and I'm making good progress. However, I'm stuck again.
I added a code to produce a dynamic overbought/oversold line on the indicator. That part works great. Now, I'm wanting to add a plotshape when conditions are met to signal long or short. Unfortunately, I can't figure out how to compare a bool variable and a float variable. The dynamic OB/OS lines are floats. The crossover lines are bool.
What I want is for the (green/long) shape to plot when a crossover happens below the dynamic oversold line and the (red/short) shape to plot when the crossunder happens above the dynamic overbought line.
I've tried all kinds of things (var, loop with "while" using sb value as a trigger). I either break the code or I get every crossover plotted. Here is what I have that is stable. I took all my junk that didn't work out:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © HammerGeek
//#version=5
indicator('Impulse MACD [HammerGeek - LazyBear]', shorttitle='IMACD_HG_LB', overlay=false)
lengthMA = input(34)
lengthSignal = input(9)
//OverBought = input.float(0.1)
//OverSold = input.float(-0.1)
calc_smma(src, len) =>
smma = 0.0
sma_1 = ta.sma(src, len)
smma := na(smma[1]) ? sma_1 : (smma[1] * (len - 1) + src) / len
smma
calc_zlema(src, length) =>
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
d = ema1 - ema2
ema1 + d
src = hlc3
hi = calc_smma(high, lengthMA)
lo = calc_smma(low, lengthMA)
mi = calc_zlema(src, lengthMA)
md = mi > hi ? mi - hi : mi < lo ? mi - lo : 0
sb = ta.sma(md, lengthSignal)
sh = md - sb
mda = ta.sma(md, lengthSignal)
OverBoughtLine = ta.highest(sb * 0.5, lengthMA*100)
OverSoldLine = ta.lowest(sb * 0.5, lengthSignal*100)
shsbCrossOver = ta.crossover(md, sb)
shsbCrossUnder = ta.crossunder(md, sb)
//mdc = src > mi ? src > hi ? color.lime : color.green : src < lo ? color.red : color.orange
mdc = color.green
plot(0, color=color.new(color.gray, 0), linewidth=1, title='MidLine')
plot(md, color=mdc, linewidth=2, title='ImpulseMACD', style=plot.style_line)
plot(sh, color=color.rgb(122, 5, 168, 40), linewidth=2, title='ImpulseHisto', style=plot.style_area)
plot(sb, color=color.rgb(255, 255, 255, transp = 40), linewidth=2, title='ImpulseMACDCDSignal')
plot(OverBoughtLine, color=color.new(#f3e032, 0), linewidth=1, title = 'Overbought Line')
plot(OverSoldLine, color=color.new(color.yellow, 0), linewidth=1, title = 'Oversold Line')
plotshape(shsbCrossOver, "LONG", shape.triangleup, location.bottom, color.new(color.green, 0))
plotshape(shsbCrossUnder, "SHORT", shape.triangledown, location.top, color.new(#cd0808, 0))
//ebc = input(false, title='Enable bar colors')
//barcolor(ebc ? mdc : na)
You cannot compare a bool with a float. It doesn't make any sense and it is not legal in pinescript.
What I want is for the (green/long) shape to plot when a crossover
happens below the dynamic oversold line and the (red/short) shape to
plot when the crossunder happens above the dynamic overbought line.
You want to check if the values you use in that crossover is under your dynamic oversold line at the time of crossover.
OverSoldLine = ta.lowest(sb * 0.5, lengthSignal*100)
shsbCrossOver = ta.crossover(md, sb)
is_good = shsbCrossOver and (md < OverSoldLine) and (sb < OverSoldLine)
Pinescript array, checking to see if array size is minimal needed, yet it is display this error enter image description here
Here is the code I am refering:
if confirmedTriangle and array.size(triangle_price)>4 and array.size(triangle_index)>4
for i = 0 to 3
//Slope
xIndexDis = array.get(triangle_index, 0) - array.get(triangle_index, 4)// : na
yPriceDis = array.get(triangle_price, 0) - array.get(triangle_price, 4)// : na
xIndexDis2 = array.get(triangle_index, 3) - array.get(triangle_index, 1)// : na
yPriceDis2 = array.get(triangle_price, 3) - array.get(triangle_price, 1)// : na
m = yPriceDis / xIndexDis //Gradient for bottom line
m2 = yPriceDis2 / xIndexDis2 // Gradient for top line
x1 = array.get(triangle_index, i)-testLength
y1 = array.get(triangle_price, i)
x2 = array.get(triangle_index, i+1)-testLength
y2 = array.get(triangle_price, i+1)
array.push(tri_array, line.new(x1,y1,x2,y2, color=color.white))
xa1 = array.get(triangle_index,4)
xa2 = array.get(triangle_index,0)
xb1 = array.get(triangle_index,3)
xb2 = array.get(triangle_index,1)
ya1 = array.get(triangle_price,4)
ya2 = array.get(triangle_price,0)
yb1 = array.get(triangle_price,3)
yb2 = array.get(triangle_price,1)
label.new(bar_index, high+atr, text="Xa1 = " + str.tostring(xa1) + " / Ya1 = " + str.tostring(ya1) + "\n Xa2 = " + str.tostring(xa2) + " / Ya2 = " + str.tostring(ya2) + "\nXb1 = " + str.tostring(xb1) + " / Yb1 = " + str.tostring(yb1) + "\n Xb2 = " + str.tostring(xb2) + " / Yb2 = " + str.tostring(yb2))
I don't get any errors on sometimeframes but the majority do present the same error,
Found the issues,
I assigned array.size(triangle_price) to a variable
e.g. arraySize = array.size(triangle_price)
In confirmedTriangle I was checking if arraySize>4, but the array size had been changed since that check
Simple fix was just to remove "arraySize = array.size(triangle_price)" and use array.size(triangle_price) in each check
I'm trying to make a map of Europe using tmap and the eurostat package.
I want to add a compass and a scale bar to the map. However they don't appear inside the graph, but outside of the map, at the bottom. Does anyone know what I'm doing wrong? I want the compass at the left top of the map, and the scale bar at the right bottom.
countries = gisco_get_countries(
year = "2016",
epsg = "3035",
resolution = "3"
)
br = c(0,40,50,65,80,150)
tm_shape(countries, bbox = c(23, 14, 74, 55) * 10e4) +
tm_fill("#E0E0E0") +
tm_shape(nuts2.sf) +
tm_fill(
"fatal_inj_30day",
breaks = br,
style = "fixed",
palette = "Blues",
alpha = .7,
title = "Fatalities per million inhabitants \n(2018-2019)"
) +
tm_compass(position = c("left","top")) +
tm_scale_bar(position = c("right","bottom")) +
tm_shape(countries) +
tm_borders(lwd = .25) +
tm_layout(
bg.color = "#F2F2F2",
outer.bg.color = "white",
legend.bg.color = "white",
legend.frame = "black",
legend.title.size = 0.8,
inner.margins = c(0, 0, 0, 0),
outer.margins = c(0, 0, 0, 0),
frame = TRUE,
frame.lwd = 0,
attr.outside = TRUE,
legend.position = c("right", "top"),
main.title = "Note: regions with 10 fatalities or less are not included in the Figure",
main.title.position = "left",
main.title.size = 0.7
)
When I try to draw a map with a Hobo–Dyer projection, it told me:
Error in st_crs.character(x[[shape.id[masterID]]]$projection) : invalid crs: hd
Here is my code:
tm_shape(countries_spdf, projection = "hd") +
tm_grid(n.x = 11, n.y = 11) +
tm_fill(col = "population", style = "quantile") +
tm_borders(col = "burlywood4")
What should I do?
I believe the projection feature of tm_shape has been changed to only take on integers representing the desired CRS. i.e. it still works with 4326, which is the most common one in the industry. However there is workaround using coord_map from ggplot library which I have included below:
tm_shape(countries_spdf, projection = 4326) +
tm_grid(n.x = 11, n.y = 11) +
tm_fill(col = "population", style = "quantile") +
tm_borders(col = "burlywood4")
tm_shape(countries_spdf, projection = coord_map("hr")) +
tm_grid(n.x = 11, n.y = 11) +
tm_fill(col = "population", style = "quantile") +
tm_borders(col = "burlywood4")
Hope this helps
I am trying to train a Siamese Lasagne model in batches of 100.
The inputs are X1 (100x3x100x100) and X2 (same size) and Y(100x1) and my last layer is a Dense layer of one output dimension as I am expecting a value of 0 or 1 as a target value. However, it is throwing an error for unexpected dimension. Below are the code excerpts:
input1 = lasagne.layers.InputLayer(shape=(None,3, 100, 100), input_var=None)
conv1_a = lasagne.layers.Conv2DLayer(input1,
num_filters=24,
filter_size=(7, 7),
nonlinearity=lasagne.nonlinearities.rectify)
pool1_a = lasagne.layers.MaxPool2DLayer(conv1_a, pool_size=(3, 3), stride=2)
Layer 2 is same as above.
Output Layer:
dense_b = lasagne.layers.DenseLayer(dense_a,
num_units=128,
nonlinearity=lasagne.nonlinearities.rectify)
dense_c = lasagne.layers.DenseLayer(dense_b,
num_units=1,
nonlinearity=lasagne.nonlinearities.softmax)
net_output = lasagne.layers.get_output(dense_c)
true_output = T.ivector('true_output')
The training code is below:
loss_value = train(X1_train,X2_train,Y_train.astype(np.int32))
print loss_value
ValueError: Input dimension mis-match. (input[0].shape[1] = 100,
input[1].shape[1] = 1) Apply node that caused the error:
Elemwise{Composite{((i0 * i1) + (i2 *
log1p((-i3))))}}(InplaceDimShuffle{x,0}.0, LogSoftmax.0,
Elemwise{sub,no_inplace}.0, SoftmaxWithBias.0) Toposort index: 113
Inputs types: [TensorType(int32, row), TensorType(float32, matrix),
TensorType(float64, row), TensorType(float32, matrix)] Inputs shapes:
[(1, 100), (100, 1), (1, 100), (100, 1)] Inputs strides: [(400, 4),
(4, 4), (800, 8), (4, 4)] Inputs values: ['not shown', 'not shown',
'not shown', 'not shown'] Outputs clients:
[[Sum{acc_dtype=float64}(Elemwise{Composite{((i0 * i1) + (i2 *
log1p((-i3))))}}.0)]]
Try using draw_net.py as follows:
import draw_net
dot = draw_net.get_pydot_graph(lasagne.layers.get_all_layers(your_last_layer),
verbose = True)
dot.write("test.pdf", format="pdf")
to dump the Lasagne graph in pdf format (requires graphviz to be installed)