manochasunilgithub / FPTrader

Open Source Trading Platform

FPTrader

Open Source Algo Trading Platform to trade various financial products (Equity/Future/FX/Index/Baskets)

Building a trading platform from scratch can be very time-consuming, frustrating and risky.Without a trading platform source code you are dealing with the black box.

FPTrader's is a container engine which manages your portfolio and data feeds letting you focus on your algorithm strategy and execution. Data is piped into your strategy for you to analyze and place trades.

FPTrader supports C# and Python programming languages making a truly open platform which can be run on linux or Windows. All CPU-intensive processes are asynchronous, taking full advantage of multi-core processors. Data loading,message processes make full use of asynchronous programming design.

FP Trader is Flexible, Customizable and Performant. It will work for retail trading, professional trading, automated trading, black box trading and more. No matter if you are a brokerage, professional trader, hedge fund or prop trader, this will provide an interface for trading.

 Features 
  - Professional charting and technical analysis.
  - Real time stock, futures and forex quote screens.
  - Easy-to-use APIs for market data, back testing and automated trading.
  - Minimum clicks to enable Trading
  - Orders can be routed to any destination.
  - Any data provider can be implemented. ( Reuters/Bloomberg/Exchange Feed)
  - All windows, toolbars, menus, charts and other features are completely customizable.
  - Containers can be run on exchange co locations.
  
  Cross platform solution
  Support for all markets 
  High performance
  Low Latency
  Direct connection — Trade is conducted via a direct connection to the exchange. Moreover, you can use the FIX protocol.

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1. Smart logging - Imagine you have 100 of strategies running and once they are deployed, monitoring these logs can become nightmare where our smart listeners and monitoring comes handy which can generate alerts and emails to keep us our trading platform robust.
2. All Server and Client components are written with keeping high performance in view.
3. TDD and Continous integration is followed through out so we can make changes in the code with full confidence.
4. Each release tests the performance by running sample strategy and generating stats to make sure there is no performance degradation.
5. Backtest can be run on any machine including cloud AWS/Azure Floats/Double Financial applications often perform comparison operations on prices, for example, comparing an aggressive market order price against a limit price in the order book to determine if a trade has occurred. As prices are expressed in dollars and cents,pounds and pence.. prices are normally represented in code as a floating point number (float or double). However, comparisons using floating point numbers are error prone (e.g. 56.0400000001 != 56.04) and yield unpredictable results.

To avoid this issue, financial applications often internally convert prices into an integer format. Not only does this ensure accurate comparison operations, but it also reduces the memory footprint and speeds up comparisons (integer operations are faster on a CPU than floating point operations).

The sub-dollar section of prices are retained in an integer format by multiplying the price by a scaling factor. For example, the price 54.25 would become 5425000000 with a scaling factor of 100,000,00. Eight decimal places are sufficient to represent the valid range of tick prices in the financial market.