princefr

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © prince_ond

//@version=4
strategy(title='[STRATEGY][UL]MA strategy', pyramiding=0, overlay=true, initial_capital=20000, calc_on_every_tick=false,
         currency=currency.USD, default_qty_type=strategy.percent_of_equity, default_qty_value=100)
ma = ema(close, 50)
ma_color() => ma[5] > ma[2] ? color.red : color.green
ma__isdown() =>  ma[5] > ma[2]
ma_is_up() =>  ma[5] < ma[2]

princefr

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © prince_ond

//@version=4
study("Mon Script", overlay=true)
ma = ema(close, 20)
ma_color() => ma[5] > ma[2] ? color.red : color.green
ma__isdown = ma[5] > ma[2]
ma_is_up = ma[5] < ma[2]

princefr

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © prince_ond

//@version=4
study("mon script", shorttitle="Spartiate")

// === INPUTS ===

uDiv       = input(true,"Show Divergence Channel")
multi      = input(0.5, minval=0.0, maxval=3.0, title="Divergence Channel Width Factor (Stddev)")

princefr

from Binance import Binance_Wrapper
from flask import Flask, request
import os
import json

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException

princefr

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © prince_ond

//@version=4
study("VWAPD[Liquidator]", overlay=true)
k = sma(vwap(close), 3)
d = sma(k, 3)

p4 = plot(k, color=color.blue, title="vwap-A", transp=100)
p5 = plot(d, color=color.orange, title="vwap-B", transp=100)

princefr

//@version=4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © pondonda


study("Divergence", shorttitle="Divergence[BBQDiv]",  overlay=true)

uHid = input(true, title="Use Hidden Divergence in Strategy")
uReg = input(true, title="Use Regular Divergence in Strategy")

princefr

//@version=3
study("Fibonacci Zone", overlay=true)
per=input(21, "calculate for last ## bars")
hl=highest(high,per) //High Line (Border)
ll=lowest(low,per)   //Low Line  (Border)
dist=hl-ll          //range of the channel    
hf=hl-dist*0.236    //Highest Fibonacci line
cfh=hl-dist*0.382    //Center High Fibonacci line
cfl=hl-dist*0.618    //Center Low Fibonacci line
lf=hl-dist*0.764     //Lowest Fibonacci line

princefr

gcloud config set compute/zone us-central1-f # change the compute zone.
ctpu up --tpu-size=v3-8 --machine-type n1-standard-8 # create a tpu v3 machine + 8 nodes

tpu succesfully created ?


connect to gcloud vm instance, if you don't have one create one

docker pull gcr.io/tpu-pytorch/xla:r0.5 # pull pytorch xla into the vm
docker run -it --shm-size 16G gcr.io/tpu-pytorch/xla:r0.5

princefr

class KeypointRCNN(FasterRCNN):
    """
    Implements Keypoint R-CNN.

    The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
    image, and should be in 0-1 range. Different images can have different sizes.

    The behavior of the model changes depending if it is in training or evaluation mode.

    During training, the model expects both the input tensors, as well as a targets (list of dictionary),

princefr

if __name__ == '__main__':
    load_model()
    # Adding some arg to a parser so its easy to manage
    parser = argparse.ArgumentParser()
    parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.')
    parser.add_argument('-wd', '--width', dest='width', type=int, default=320, help='Width of the frames in the video stream.')
    parser.add_argument('-ht', '--height', dest='height', type=int, default=360, help='Height of the frames in the video stream.')
    args = parser.parse_args()