I need to be able to extract historical candlestick data (such as Open, Close, High, Low, and Volume) of a candlestick in differing intervals (1m, 3m, 5m, 1H, etc.) at a specified time (timestamps) from Phemex.
Other exchanges, such as Binance or FTX, seem to provide REST Websocket API for this, yet I can't seem to find one for Phemex. Mind helping me resolve this issue? Thank you so much.
Steps I have taken, yet found no resolution:
Went to https://phemex.com/user-guides/api-overview
Went to https://github.com/phemex/phemex-api-docs/blob/master/Public-Contract-API-en.md
None of the items listed in 'Market Data API List' seem to do the task
This code will get the candels and save them to a csv file. Hope this helps:)
exchange = ccxt.phemex({
'options': { 'defaultType': 'swap' },
'enableRateLimit': True
})
# Load the markets
markets = exchange.load_markets()
curent_time = int(time.time()*1000)
one_min = 60000
def get_all_candels(symbol,start_time,stop_time):
counter = 0
candel_counter = 0
data_set = []
t = 0
while t < stop_time:
if data_set == []:
block = exchange.fetch_ohlcv(symbol,'1m',start_time)
for candle in block:
if candle == []:
break
data_set.append(candle)
last_time_in_block = block[-1][0]
counter += 1
candel_counter += len(block)
print(f'{counter} - {block[0]} - {candel_counter} - {last_time_in_block}')
if data_set != []:
t = last_time_in_block + one_min
block = exchange.fetch_ohlcv(symbol,'1m',t)
if block == []:
break
for candle in block:
if candle == []:
break
data_set.append(candle)
last_time_in_block = block[-1][0]
candel_counter += len(block)
counter += 1
print(f'{counter} - {block[0]} - {candel_counter} - {last_time_in_block}')
time.sleep(1)
return data_set
data_set = get_all_candels('BTCUSD',1574726400000,curent_time)
print(np.shape(data_set))
with open('raw.csv', 'w', newline='') as csv_file:
column_names = ['time', 'open', 'high', 'low', 'close', 'volume']
csv_writer = csv.DictWriter(csv_file,fieldnames=column_names)
csv_writer.writeheader()
for candel in data_set:
csv_writer.writerow({
'time':candel[0],
'open':candel[1],
'high':candel[2],
'low':candel[3],
'close':candel[4],
'volume':candel[5]
})
Related
I'm trying to create a gold table notebook in Databricks, however it would take 9 days to fully reprocess the historical data (43GB, 35k parquet files). I tried scaling up the cluster but it doesn't go above 5000 records/second. The bottleneck seems to be the applyInPandas() function. I'm wondering if I could replace pandas with anything else to make the gold notebook execute faster.
Silver table has 60 columns (read_id, reader_id, tracker_timestamp, event_type, ebook_id, page_id, agent_ip, agent_device_type, ...). Each row of data represents read event of an ebook. E.g 'page turn', 'click on image', 'click on link',... All of the events that have occurred in the single session have the same read.id. In the gold table I'm trying to group those events in sessions and calculate the number of times each event has occurred in the single session. So instead of 100+ rows of data for a read session in silver table I would end up just with a single aggregated row in gold table.
Input is the silver delta table:
import pyspark.sql.functions as F
import pyspark.sql.types as T
import pandas as pd
from pyspark.sql.functions import pandas_udf
input = (spark
.readStream
.format("delta")
.option("withEventTimeOrder", "true")
.option("maxFilesPerTrigger", 100)
.load(f"path_to_silver_bucket")
)
I use withWatermark and session_window functions to ensure I end up grouping all of the events from the single read session. (read session automatically ends 30 minutes after the last reader activity)
group = input.withWatermark("tracker_timestamp", "10 minutes").groupBy("read_id", F.session_window(input.tracker_timestamp, "30 minutes"))
In the next step I use the applyInPandas function like so:
sessions = group.applyInPandas(processing_function, schema=processing_function_output_schema)
Definition of the processing_function used in applyInPandas:
def processing_function(df):
surf_time_ms = df.query('event_type == "surf"')['duration'].sum()
immerse_time_ms = df.query('event_type == "immersion"')['duration'].sum()
min_timestamp = df['tracker_timestamp'].min()
max_timestamp = df['tracker_timestamp'].max()
shares = len(df.query('event_type == "share"'))
leads = len(df.query('event_type == "lead_store"'))
is_read = len(df.query('event_type == "surf"')) > 0
distinct_pages = df['page_id'].nunique()
data = {
"read_id": df['read_id'].values[0],
"surf_time_ms": surf_time_ms,
"immerse_time_ms": immerse_time_ms,
"min_timestamp": min_timestamp,
"max_timestamp": max_timestamp,
"shares": shares,
"leads": leads,
"is_read": is_read,
"number_of_events": len(df),
"distinct_pages": distinct_pages
}
for field in not_calculated_string_fields:
data[field] = df[field].values[0]
new_df = pd.DataFrame(data=data, index=['read_id'])
for x in all_events:
new_df[f"count_{x}"] = df.query(f"type == '{x}'").count()
for x in duration_events:
duration = df.query(f"event_type == '{x}'")['duration']
duration_sum = duration.sum()
new_df[f"duration_{x}_ms"] = duration_sum
if duration_sum > 0:
new_df[f"mean_duration_{x}_ms"] = duration.mean()
else:
new_df[f"mean_duration_{x}_ms"] = 0
return new_df
And finally, I'm writing the calculated row to the gold table like so:
for_partitioning = (sessions
.withColumn("tenant", F.col("story_tenant"))
.withColumn("year", F.year(F.col("min_timestamp")))
.withColumn("month", F.month(F.col("min_timestamp"))))
checkpoint_path = "checkpoint-path"
gold_path = f"gold-bucket"
(for_partitioning
.writeStream
.format('delta')
.partitionBy('year', 'month', 'tenant')
.option("mergeSchema", "true")
.option("checkpointLocation", checkpoint_path)
.outputMode("append")
.start(gold_path))
Can anybody think of a more efficient way to do a UDF in PySpark than applyInPandas for the above example? I simply cannot afford to wait 9 days to reprocess 43GB of data...
I've tried playing around with different input and output options (e.g. .option("maxFilesPerTrigger", 100)) but the real problem seems to be applyInPandas.
You could rewrite your processing_function into native Spark if you really wanted.
"read_id": df['read_id'].values[0]
F.first('read_id').alias('read_id')
"surf_time_ms": df.query('event_type == "surf"')['duration'].sum()
F.sum(F.when(F.col('event_type') == 'surf', F.col('duration'))).alias('surf_time_ms')
"immerse_time_ms": df.query('event_type == "immersion"')['duration'].sum()
F.sum(F.when(F.col('event_type') == 'immersion', F.col('duration'))).alias('immerse_time_ms')
"min_timestamp": df['tracker_timestamp'].min()
F.min('tracker_timestamp').alias('min_timestamp')
"max_timestamp": df['tracker_timestamp'].max()
F.max('tracker_timestamp').alias('max_timestamp')
"shares": len(df.query('event_type == "share"'))
F.count(F.when(F.col('event_type') == 'share', F.lit(1))).alias('shares')
"leads": len(df.query('event_type == "lead_store"'))
F.count(F.when(F.col('event_type') == 'lead_store', F.lit(1))).alias('leads')
"is_read": len(df.query('event_type == "surf"')) > 0
(F.count(F.when(F.col('event_type') == 'surf', F.lit(1))) > 0).alias('is_read')
"number_of_events": len(df)
F.count(F.lit(1)).alias('number_of_events')
"distinct_pages": df['page_id'].nunique()
F.countDistinct('page_id').alias('distinct_pages')
for field in not_calculated_string_fields:
data[field] = df[field].values[0]
*[F.first(field).alias(field) for field in not_calculated_string_fields]
for x in all_events:
new_df[f"count_{x}"] = df.query(f"type == '{x}'").count()
The above can probably be skipped? As far as my tests go, new columns get NaN values, because .count() returns a Series object instead of one simple value.
for x in duration_events:
duration = df.query(f"event_type == '{x}'")['duration']
duration_sum = duration.sum()
new_df[f"duration_{x}_ms"] = duration_sum
if duration_sum > 0:
new_df[f"mean_duration_{x}_ms"] = duration.mean()
else:
new_df[f"mean_duration_{x}_ms"] = 0
*[F.sum(F.when(F.col('event_type') == x, F.col('duration'))).alias(f"duration_{x}_ms") for x in duration_events]
*[F.mean(F.when(F.col('event_type') == x, F.col('duration'))).alias(f"mean_duration_{x}_ms") for x in duration_events]
So, instead of
def processing_function(df):
...
...
sessions = group.applyInPandas(processing_function, schema=processing_function_output_schema)
you could use efficient native Spark:
sessions = group.agg(
F.first('read_id').alias('read_id'),
F.sum(F.when(F.col('event_type') == 'surf', F.col('duration'))).alias('surf_time_ms'),
F.sum(F.when(F.col('event_type') == 'immersion', F.col('duration'))).alias('immerse_time_ms'),
F.min('tracker_timestamp').alias('min_timestamp'),
F.max('tracker_timestamp').alias('max_timestamp'),
F.count(F.when(F.col('event_type') == 'share', F.lit(1))).alias('shares'),
F.count(F.when(F.col('event_type') == 'lead_store', F.lit(1))).alias('leads'),
(F.count(F.when(F.col('event_type') == 'surf', F.lit(1))) > 0).alias('is_read'),
F.count(F.lit(1)).alias('number_of_events'),
F.countDistinct('page_id').alias('distinct_pages'),
*[F.first(field).alias(field) for field in not_calculated_string_fields],
# skipped count_{x}
*[F.sum(F.when(F.col('event_type') == x, F.col('duration'))).alias(f"duration_{x}_ms") for x in duration_events],
*[F.mean(F.when(F.col('event_type') == x, F.col('duration'))).alias(f"mean_duration_{x}_ms") for x in duration_events],
)
So I am trying to make this continue from get_burger_choice to get_burger price to where it would flow and finish the program. I am trying to write this program to where I could get the type of burger then the price from get_burger_price. Heres the problem, I do not know how to intergrate it towards doing that. I am completely stuck after the part where it says "no" at the end part of get_burger_choice to where I can transition to get_burger_price
Here is the code that I used : the part highlighted is where Im having trouble transitioning:I would like it to where if the user says no, it would ask if they would like to see the price, and it would transition towards get_burger_price
def get_burger_choice():
SIZE = 3
burgers = ["Cheesy", "Regular", "Veggie" ]
search = True
while search == True:
index = 0
found = False
print("What type of burger would you like?")
searchValue = input()
while found == False and index <= SIZE - 1:
if burgers[index].lower()==searchValue.lower():
found = True
else:
index = index + 1
if found:
print("That is on our menu")
else:
print("That is not in our menu")
print("Do you want see another burger? yes/no: ")
tryagain = input()
if tryagain.lower() =='no':
print("Would you like to see the price? yes or no: ")
if
def price_burger(burger_choice, burger_price):
if burger_price == 'cheesy':
burger_price = 3.00
elif burger_choice == 'regular':
burger_price = 2.00
elif burger_choice == 'veggie':
burger_price = 2.00
else:
print("we do not have that, sorry")
return burger_price
def total_price(burger_price, burger_choice=None):
print("The burger cost $", burger_price)
def closing(burger_choice):
if burger_choice == 'cheesy':
print("Nice selection, This is our best!")
else:
print("enjoy!")
def main(burger_price = 0):
choice = "yes"
while choice != "no":
burger_choice = get_burger_choice()
burger_price = price_burger(burger_choice,burger_price)
total_price(burger_price)
closing(burger_choice)
choice = input("\nWould you like to try again? yes or no: ")
main()
Background:
I have writing a crypto trading bot for fun and profit.
So far, it connects to an exchange and gets streaming price data.
I am using this price to create a technical indicator (MACD).
Generally for MACD, it is recommended to use closing prices for 26, 12 and 9 days.
However, for my trading strategy, I plan to use data for 26, 12 and 9 minutes.
Question:
I am getting multiple (say 10) price ticks in a minute.
Do I simply average them and round the time to the next minute (so they all fall in the same minute bucket)? Or is there is better way to handle this.
Many Thanks!
This is how I handled it. Streaming data comes in < 1s period. Code checks for new low and high during streaming period and builds the candle. Probably ugly since I'm not a trained developer, but it works.
Adjust "...round('20s')" and "if dur > 15:" for whatever candle period you want.
def on_message(self, msg):
df = pd.json_normalize(msg, record_prefix=msg['type'])
df['date'] = df['time']
df['price'] = df['price'].astype(float)
df['low'] = df['low'].astype(float)
for i in range(0, len(self.df)):
if i == (len(self.df) - 1):
self.rounded_time = self.df['date'][i]
self.rounded_time = pd.to_datetime(self.rounded_time).round('20s')
self.lhigh = self.df['price'][i]
self.lhighcandle = self.candle['high'][i]
self.llow = self.df['price'][i]
self.lowcandle = self.candle['low'][i]
self.close = self.df['price'][i]
if self.lhigh > self.lhighcandle:
nhigh = self.lhigh
else:
nhigh = self.lhighcandle
if self.llow < self.lowcandle:
nlow = self.llow
else:
nlow = self.lowcandle
newdata = pd.DataFrame.from_dict({
'date': self.df['date'],
'tkr': tkr,
'open': self.df.price.iloc[0],
'high': nhigh,
'low': nlow,
'close': self.close,
'vol': self.df['last_size']})
self.candle = self.candle.append(newdata, ignore_index=True).fillna(0)
if ctime > self.rounded_time:
closeit = True
self.en = time.time()
if closeit:
dur = (self.en - self.st)
if dur > 15:
self.st = time.time()
out = self.candle[-1:]
out.to_sql(tkr, cnx, if_exists='append')
dat = ['tkr', 0, 0, 100000, 0, 0]
self.candle = pd.DataFrame([dat], columns=['tkr', 'open', 'high', 'low', 'close', 'vol'])
As far as I know, most or all technical indicator formulas rely on same-sized bars to produce accurate and meaningful results. You'll have to do some data transformation. Here's an example of an aggregation technique that uses quantization to get all your bars into uniform sizes. It will convert small bar sizes to larger bar sizes; e.g. second to minute bars.
// C#, see link above for more info
quoteHistory
.OrderBy(x => x.Date)
.GroupBy(x => x.Date.RoundDown(newPeriod))
.Select(x => new Quote
{
Date = x.Key,
Open = x.First().Open,
High = x.Max(t => t.High),
Low = x.Min(t => t.Low),
Close = x.Last().Close,
Volume = x.Sum(t => t.Volume)
});
See Stock.Indicators for .NET for indicators and related tools.
I need to request data periodically from a configurable number of devices at configurable intervals (per device). All devices are connected to a shared data bus, so only one device can send data at the same time.
The devices have very little memory, so each device can only keep the data for a certain period of time before it is overwritten by the next chunk. This means I need to make sure to request data from any given device while it is still available, or else it will be lost.
I am looking for an algorithm that, given a list of devices and their respective timing properties, finds a feasible schedule in order to achieve minimal data loss.
I guess each device could be formally described using the following properties:
data_interval: time it takes for the next chunk of data to become available
max_request_interval: maximum amount of time between requests that will not cause data loss
processing_time: time it takes to send a request and fully receive the corresponding response containing the requested data
Basically, I need to make sure to request data from every device once its data is ready and not yet expired, while keeping in mind the deadlines for all other devices.
Is there some sort of algorithm for this kind of problem? I highly doubt I'm the first person to ever encounter a situation like this. Searching for existing solutions online didn't yield many useful results, mainly because scheduling algorithms are mostly used for operating systems and such, where scheduled processes can be paused and resumed at will. I can't do this in my case, however, since the process of requesting and receiving a chunk of data is atomic, i.e. it can only be performed in its entirety or not at all.
I solved this problem using non-preemptive deadline monotonic scheduling.
Here's some python code for anyone interested:
"""This module implements non-preemptive deadline monotonic scheduling (NPDMS) to compute a schedule of periodic,
non-preemptable requests to slave devices connected to a shared data bus"""
from math import gcd
from functools import reduce
from typing import List
class Slave:
def __init__(self, name: str, period: int, processing_time: int, offset=0, deadline=None):
self.name = name
self.period = int(period)
self.processing_time = int(processing_time)
self.offset = int(offset)
if self.offset >= self.period:
raise ValueError("Slave %s: offset must be < period" % name)
self.deadline = int(deadline) if deadline else self.period
if self.deadline > self.period:
raise ValueError("Slave %s: deadline must be <= period" % name)
class Request:
def __init__(self, slave: Slave, start_time: int):
self.slave = slave
self.start_time = start_time
self.end_time = start_time + slave.processing_time
self.duration = self.end_time - self.start_time
def overlaps_with(self, other: 'Request'):
min_duration = self.duration + other.duration
start = min(other.start_time, self.start_time)
end = max(other.end_time, self.end_time)
effective_duration = end - start
return effective_duration < min_duration
class Scenario:
def __init__(self, *slaves: Slave):
self.slaves = list(slaves)
self.slaves.sort(key=lambda slave: slave.deadline)
# LCM of all slave periods
self.cycle_period = reduce(lambda a, b: a * b // gcd(a, b), [slave.period for slave in slaves])
def compute_schedule(self, resolution=1) -> 'Schedule':
request_pool = []
for t in range(0, self.cycle_period, resolution):
for slave in self.slaves:
if (t - slave.offset) % slave.period == 0 and t >= slave.offset:
request_pool.append(Request(slave, t))
request_pool.reverse()
scheduled_requests = []
current_request = request_pool.pop()
t = current_request.start_time
while t < self.cycle_period:
ongoing_request = Request(current_request.slave, t)
while ongoing_request.start_time <= t < ongoing_request.end_time:
t += resolution
scheduled_requests.append(ongoing_request)
if len(request_pool):
current_request = request_pool.pop()
t = max(current_request.start_time, t)
else:
current_request = None
break
if current_request:
request_pool.append(current_request)
return Schedule(self, scheduled_requests, request_pool)
class Schedule:
def __init__(self, scenario: Scenario, requests: List[Request], unscheduled: List[Request] = None):
self.scenario = scenario
self.requests = requests
self.unscheduled_requests = unscheduled if unscheduled else []
self._utilization = 0
for slave in self.scenario.slaves:
self._utilization += float(slave.processing_time) / float(slave.period)
self._missed_deadlines_dict = {}
for slave in self.scenario.slaves:
periods = scenario.cycle_period // slave.period
missed_deadlines = []
for period in range(periods):
start = period * slave.period
end = start + slave.period
request = self._find_request(slave, start, end)
if request:
if request.start_time < (start + slave.offset) or request.end_time > start + slave.deadline:
missed_deadlines.append(request)
if missed_deadlines:
self._missed_deadlines_dict[slave] = missed_deadlines
self._overlapping_requests = []
for i in range(0, len(requests)):
if i == 0:
continue
previous_request = requests[i - 1]
current_request = requests[i]
if current_request.overlaps_with(previous_request):
self._overlapping_requests.append((current_request, previous_request))
self._incomplete_requests = []
for request in self.requests:
if request.duration < request.slave.processing_time:
self._incomplete_requests.append(request)
#property
def is_feasible(self) -> bool:
return self.utilization <= 1 \
and not self.has_missed_deadlines \
and not self.has_overlapping_requests \
and not self.has_unscheduled_requests \
and not self.has_incomplete_requests
#property
def utilization(self) -> float:
return self._utilization
#property
def has_missed_deadlines(self) -> bool:
return len(self._missed_deadlines_dict) > 0
#property
def has_overlapping_requests(self) -> bool:
return len(self._overlapping_requests) > 0
#property
def has_unscheduled_requests(self) -> bool:
return len(self.unscheduled_requests) > 0
#property
def has_incomplete_requests(self) -> bool:
return len(self._incomplete_requests) > 0
def _find_request(self, slave, start, end) -> [Request, None]:
for r in self.requests:
if r.slave == slave and r.start_time >= start and r.end_time < end:
return r
return None
def read_scenario(file) -> Scenario:
from csv import DictReader
return Scenario(*[Slave(**row) for row in DictReader(file)])
def write_schedule(schedule: Schedule, file):
from csv import DictWriter
writer = DictWriter(file, fieldnames=["name", "start", "end"])
writer.writeheader()
for request in schedule.requests:
writer.writerow({"name": request.slave.name, "start": request.start_time, "end": request.end_time})
if __name__ == '__main__':
import argparse
import sys
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter,
description='Use non-preemptive deadline monotonic scheduling (NPDMS) to\n'
'compute a schedule of periodic, non-preemptable requests to\n'
'slave devices connected to a shared data bus.\n\n'
'Prints the computed schedule to stdout as CSV. Returns with\n'
'exit code 0 if the schedule is feasible, else 1.')
parser.add_argument("csv_file", metavar="SCENARIO", type=str,
help="A csv file describing the scenario, i.e. a list\n"
"of slave devices with the following properties:\n"
"* name: name/id of the slave device\n\n"
"* period: duration of the period of time during\n"
" which requests must be dispatched\n\n"
"* processing_time: amount of time it takes to\n"
" fully process a request (worst-case)\n\n"
"* offset: offset for initial phase-shifting\n"
" (default: 0)\n\n"
"* deadline: amount of time during which data is\n"
" available after the start of each period\n"
" (default: <period>)")
parser.add_argument("-r", "--resolution", type=int, default=1,
help="The resolution used to simulate the passage of time (default: 1)")
args = parser.parse_args()
with open(args.csv_file, 'r') as f:
schedule = read_scenario(f).compute_schedule(args.resolution)
write_schedule(schedule, sys.stdout)
exit(0 if schedule.is_feasible else 1)
so I am very new to coding in general and I am trying to make a vertically-scrolling endless runner which basically involves jumping onto platforms to stay alive.I want to generate the same platform in three different locations endlessly. I basically copied some code from an article on the internet and then changed it around to try to make it suit my needs. However, when I run my code in the simulator, one platform is generated in the same location and no others appear. Also, when I look at the console, random numbers do appear. here is the code I am using
local blocks = display.newGroup ()
local groundMin = 200
local groundMax = 100
local groundLevel = groundMin
local function blockgenerate( event )
for a = 1, 1, -1 do
isDone = false
numGen = math.random(3)
local newBlock
print (numGen)
if (numGen == 1 and isDone == false) then
newBlock = display.newImage ("platform.jpg")
end
if (numGen == 2 and isDone == false) then
newBlock = display.newImage ("platform.jpg")
end
if (numGen == 3 and isDone == false) then
newBlock = display.newImage ("platform.jpg")
end
newBlock.name = ("block" .. a)
newBlock.id = a
newBlock.x = (a * 100) - 100
newBlock.y = groundLevel
blocks : insert(newBlock)
end
end
timer.performWithDelay (1000, blockgenerate, -1)
thank you very much in advance and sorry my description was so long
Your "a" variable is always going to be 1. Perhaps you meant to use:
a = a + 1