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recfest Test

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Disclaimer: Trading options is volatile. Please do your own due diligence. Trading in the real market involves real financial risk. There is always the potential of loss when investing/trading in any financial asset. AlgoGrow, its advisors, trainers, companies (Source91 Network Pvt Ltd or others) or any individuals associated with it (including Roshan Chandy and Akhilesh Chandy) are not liable for any potential financial losses incurred from (including but not limited to) algo trading and rules, telegram / WhatsApp group and its members. By enrolling with us you fully understand and accept these risks. It is critical to understand that fees paid are non-refundable. AlgoGrow, its trainers and those associated with the training will not be liable for your personal or emotional well-being or for any other negative impact to you. We also assume no responsibility for any actions, activity or trades that cause loss. Please do your own due diligence before trading. Trade only with money you can afford to lose. Past back tests/performance is not indicative of future profits. Market conditions do change. Strategy may need to be re-optimized. We are not SEBI registered. Our business model will never fit SEBI’s RIA regulations. There is no alternate framework yet established by SEBI for our business model to register. The latest update by SEBI on algorithmic trading is here https://www.sebi.gov.in/reports-and-statistics/reports/dec-2021/consultation-paper-on-algorithmic-trading-by-retail-investors_54515.html. We use Tradetron as a service provider to execute our trades across all our clients. We do not have control over their platform, errors on which can lead to unpredictable results. There may be errors in the brokers’ API interface which may lead to losses in trades over which we have no control over. There is no possibility of us manually taking over the trades in case of errors. Our strategies may be extremely overfitted or curve fitted. We do not perform out of sample/walk forward/noise/monte carlo tests because of limited data points to obtain a statistically significant result.

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