INTRADAY COMMODITY TRADING OPTIMIZATION USING S.A.R. CHANNELS ON NSE
The advancement of AI-based ALGO-TRADE technology has prompted enterprises worldwide to adapt their operational strategies. This novel technological advancement empowers investors to increase their chances of success and reduce their reliance on luck. In certain situations, traders use the buy-or-write approach when they have only one remaining commodity. The research methodology employed here is the "buy-and-sell maximization study design," which generates subjective evaluations. In this study, we developed ALGO-TRADE programs using Stoller Average Range Channels (S.A.R.). In finance, S.A.R. is a technical indicator visually representing a trading range's upper and lower boundaries. It constructs price-bounding envelopes by drawing lines one standard deviation above and below a simple moving average of prices. The standard deviation utilization determines the Band's magnitude, making it responsive to market fluctuations. Employing S.A.R. assists in determining whether the current price significantly deviates from the expected range. Many individuals commonly use both upper and lower bands in conjunction with a moving average. Furthermore, their design intended for these bands to complement each other, and using them independently may compromise their functionality. It is essential to incorporate supplementary indicators that exhibit strong compatibility and thoroughly verify their findings. The investigation also analyzed the financial gains and losses the commodity trading account incurred during the fiscal years 2021 and 2022. According to the results, an individual who trades with an initial investment of Rs 1,00,000 has the potential to generate a profit of Rs 60,000.
ALGO-TRADE, A.I., S.A.R, GARCH model