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Trade Digital Assets Systematically

An end to end Algorithmic Trading Platform, harnessing the power of cloud computing, offering quants infrastructure as a service to build, backtest and deploy systematic strategies on collocated racks & single codebase directly from browser offering seamless integrations with leading custody and core banking providers.. 

A comprehensive ecosystem,
to get you going

Design and test your strategy on our free data and when you're ready deploy it live to your brokerage. Code in multiple programming languages and harness our cluster of hundreds of servers to run your backtest to analyse your strategy in Equities, FX, Crypto, CFD, Options or Futures Markets.


Order Execution & Management

Whitebox solution providing advanced Order Execution & portfolio Management (OEMS) for all digital assets. Take full control of your spot and derivative assets held across your accounts and portfolios with accurate and timely decision support capabilities to maximize returns and alpha generation

Strategy & Trade Lifecycle

Design strategies from scratch using browser based IDE with multiple API Options (Rest, Websocket and FIX).  Benefit from live collocated or solo trading environment. Execute on one of the 9 supported brokers, 20+ exchanges and high frequency data feeds. Trade Crypto, Equities, Commodities & FX systematically with ease. Get Real time notifications with Websocket API.



Data from 20+ exchanges & adding more

Build your own alpha or choose from Quantl Library & Backtest

Connect your broker & securely publish your alpha

Build your quant strategies from scratch and benefit from unlimited flexibility.

Platform independent, lightweight Quantl components utilising Docker.

Dedicated Cloud instance on Quantl managed servers

Brower based IDE for advanced traders supporting 10 programming languages

Super Data API providing Rest endpoints or Websocket for real time data, or use FIX for advance DMA

Data from 10+ stock and commodities exchanges worldwide

Connect your GitHub & import existing strategies, or code in Python, JavaScript, Rust or C++ 

from .momentum_class import *

from .DirectionalHedge import *

class NumpyArrayEncoder(JSONEncoder): def default(self, obj): if isinstance(obj, np.ndarray):

      return obj.tolist() return JSONEncoder.default(self, obj)

class BacktestBase(object):

def __init__(self, symbol, start, end, amount, ftc=0.0, ptc=0.0, verbose=True): self.symbol = symbol

self.start = start

self.end = end

self.initial_amount = amount

self.amount = amount

self.ftc = ftc

def place_buy_order(self, bar, units=None, amount=None):

''' Place a buy order. '''

    date, price = self.get_date_price(bar)

    if units is None:

       units = int(amount / price) self.amount -= (units * price) * (1 + self.ptc) + self.ftc
               self.units +=           
self.trades += 1

    if self.verbose: print(f'{date} | selling {units} units at {price:.2f}')    /                                self.print_balance(bar) self.print_net_wealth(bar)

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