Quantman Premium Strategy
Reginald Hanfy
In the dynamic world of trading, the ability to adapt and comprehend market dynamics is foremost to staying ahead. In a recent dialogue with a seasoned trading strategist, we explored a fundamental question:
My own misadventure involved developing a strategy based on a limited dataset, only to suffer significant losses when market conditions naturally shifted. The lesson learned is clear: a robust trading strategy demands testing across diverse market cycles to ensure its adaptability.
My argument is that if a strategy undergoes thorough testing across different market cycles, the likelihood of failure decreases. The true challenge arises when traders test strategies with limited data or within a short timeframe. I recall spending months developing a strategy, only to realize its lack of practicality due to curve fitting.
These metrics includes important aspects such as risk per trade, maximum drawdown, average profit, average loss, Winning probability, and Winning/Losing Streak for the backtested system, etc. Armed with this data, one can make informed decisions about the potential future performance of the trading strategy.
Algorithmic trading, often referred to as algo trading, can be profitable in India, but success in this field depends on various factors and requires a deep understanding of financial markets, trading strategies, and risk management.
The success of an algorithmic trading strategy depends on a number of factors, including the skill of the trader who developed the strategy, the quality of the data used, and the volatility of the market. Here are key considerations:
Market Knowledge: Success in algo trading in India, as anywhere, begins with a comprehensive grasp of the Indian financial markets. Traders must stay aware of market trends, economic indicators, and industry-specific news.
Technology and Infrastructure: Algo trading heavily depends on rapid and reliable technology and infrastructure. A sturdy trading system and low-latency connections are imperative for executing strategies efficiently.
Risk Management: Effective risk management is critical. Algo traders must practice risk controls and stop-loss mechanisms to safeguard their investments. Indian markets, like any others, can be volatile, and losses can amplify quickly without proper risk management.
Regulations: Algo trading in India is subject to regulations imposed by the Securities and Exchange Board of India (SEBI). Traders must adhere to these regulations and guarantee that their strategies remain within legal boundaries.
Capital Requirements: Algo trading typically demands a substantial capital investment to be profitable. Traders need to have adequate funds to meet margin requirements, withstand losses, and scale their operations.
Competition: The algo trading landscape in India is highly competitive, with participation from both institutional and retail traders. Maintaining a competitive edge requires innovation and continual optimization of trading strategies.
Costs: Algo trading in India incurs expenses, such as brokerage fees, exchange fees, and technology costs. These costs can impact profitability and should be integrated into your trading plan.
Psychological Discipline: Even though algo trading is automated, traders must possess the psychological discipline to adhere to their strategies and avoid impulsive decisions, particularly during periods of losses.
In summary, while algo trading can indeed be profitable in India, it is not a guaranteed path to success. It requires a combination of market understanding, technological infrastructure, well-researched strategies, risk management, and discipline.
Faster execution: Algo trading can execute trades much faster than human traders can. This is because algorithms can monitor multiple markets and trade on multiple instruments simultaneously.
Backtesting: Algo trading strategies can be backtested on historical data to see how they would have performed in the past. This can help traders to identify and refine their strategies before risking real money.
Quantman provides zero code trading platform for systematic traders/quant traders who want to create & backtest their own trading rules (Simple & Complex Strategies) to validate their trading ideas. Quantman provides 50+ indicators (Renko, Heiken-Ashi, Supertrend Trend, Trend Indicators, Momentum Indicators, Volatility Indicators, Chart Patterns, etc.,) and manages the entire 4.5 years of Authentic Exchange Data in the cloud so that users can focus on their trading strategy without bothering about GBs of data to analyze the markets.
Quantman comes up with Two modes (Basic, Advance Mode). The basic mode is for the users who want to configure trading strategies with logic controls, time-based & stop-loss, target, trailing-stop controls.
Which Advance mode one can start building multi timeframe indicators and to create complex logical calculations. One can use advance mode to apply strategies on top of different charting types (Candles, Heikinashi, Renko), Multi timeframe strategies.
Interacted with the Quantman development team, really they are passionate people about the markets and hope more interesting and important features will be released in the future and all thanks for bringing such a simplicity to a common trader to build complex trading strategies faster.
In this world of Algorithmic trading, choosing the right algorithmic trading platform is crucial to ensure your trades are executed in minimal time, and at the best conditions and your strategy is implemented properly in the algorithmic trading platform with least amount of errors, in other words choosing the right platform is necessary to increase your trading success. Quantman and Tradetron are two of the most popular algorithmic trading platform which we will be comparing in this article. Both the platforms are used to automate your algorithmic trading strategies. In this article we will compare the most crucial features such as ease of use, pricing and overall performance. Below are the 5 factors we have taken into account for the comparison.
Quantman is designed for professional traders with a focus on Quantitative analysts. It offers most of the features offered by Tradetron such as backtesting, optimisation and deplyment of trading strategies.
Tradetron is a cloud-based algorithmic trading platform which is suitable for both beginner and advanced traders due to a marketplace of strategies. It offers user friendly features as well as pre-made strategies which can be deployed directly from the marketplace so that you do not have to make one of your own.
Sometimes a trader might be busy with personal issues which require him to be away from the console. At such times it is difficult to wait till 8:30 and 8:45 to ensure that the algo's run for the day.
Can you please remove the restriction of broker login and strategy activation only after 8:30 and 8:45 respectively? Other platforms like Quantman do not have this and can be logged into anytime after the start of the new day.
Do you still have this limitations? I am trading from US. I have to wake up every night to login. Can you please check and let us know on when this will be improved ? It will potentially help all the NRI traders and increase your business as well.
The platform is built on advanced analytics and enables traders to create their own strategy in tough markets by back testing their ideas with past 5+ years data for Nifty, Bank Nifty, Equity and other instruments.
Quantman is a complete system-based trading company with a team of highly skilled professionals. The Quantman team is excited to bring you a complete system that will allow you to trade with great opportunities and avoid losses by using suitable strategy that suits to your trading style.
I'm a relatively small investor, and I'm interested in building my own fully-automated quantitative trading strategy. I also read about dark pools, and how difficult it is to get good prices on orders.
My question is, in the context of the other players on the market, how much capital is necessary to create a viable trading strategy? I realize that getting a high profit margin will be nearly impossible.
Ha, interesting, so many responses with "negative" expectations. There are plenty of people that have successfully gone down this road and are producing pretty nice returns, so obviously it is possible.
A trader with a smaller capital has better chances of producing good ROC with very reasonable risk parameters, simply because he's would not be constrained by liquidity and ability to deploy capital. In fact, for someone trying to deploy a million or two, there is a number of capacity-constrained opportunities that can easily produce high double digit returns with Sharpe ratio north of 2.
Market data and hardware are pretty cheap these days. You can get tick-level data for 10k and you can get 1 min bar (all that most non-HFT systems would need) for under 5k. A reasonably powerful box will run you another 2k. Pretty much all software need are covered by FOSS.
So, to answer your own question, assume that you can produce returns around 30 percent with Sharpe of 2 (a very reasonable assumption for small capital deployment) and that you need about $500 a month in data fees and various fixed costs (e.g. co-location, brokerage charges, new hardware etc). In short, if you have the requisite IT and quant skills plus the requires market experience, you can do it on a very tight budget. In fact, I know people who do this sort of stuff and they started at 300k.
First, building your "AI bot" which I would rather call a systematic algorithm not only requires programming skills, it also means having access to market data and, if you want to make it fully automatized, access to brokers APIs to send your orders. All this costs money, so if you're not a big, big investor or a firm, it's not likely that you will have the sufficient fund to really tackle the world of systematic trading. Don't even start thinking about feeding prices manually to a systematic strategy as an individual; you'll go on holidays or get drunk some night and forget to do it which will screw it up.
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