This strategy, skillfully crafted by script expert Snehashish, innovatively combines the strengths of the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI) to identify optimal entry and exit points in the market. The approach is meticulously designed to enter a long trade precisely when the MACD line crosses above the signal line, provided that the RSI indicated an oversold condition in the market just 5 candles prior. This timing ensures that the strategy capitalizes on the initial signs of market recovery after a sell-off, as indicated by the MACD crossover.
For closing positions, the strategy employs two critical conditions to signal an exit. First, the trade concludes when the MACD histogram is above zero, and the MACD line crosses below the signal line, indicating a potential reversal in upward momentum. Second, an exit signal is generated if the RSI was found to be in an overbought state 5 candles before, suggesting that the market may have reached a peak and could be headed for a downturn.
Snehashish’s method elegantly combines these technical indicators, filtering out noise by waiting for confirmation from both MACD and RSI under specific conditions, aiming for trades with a higher probability of success. This strategic combination seeks to optimize entry and exit points, potentially enhancing the profitability of trades by leveraging the strengths of the indicators to mitigate risks associated with market volatility.
The core principle of this strategy is to combine the MACD and RSI technical indicators to capture market turning points with greater precision. The strategy enters a long trade when the RSI shows that the market has been oversold in the recent candles, followed by the MACD line crossing above the signal line. This combination ensures that the strategy opens a position as soon as the price action shows early signs of a potential reversal.
For closing positions, the strategy focuses on potential trend reversal signals indicated by the MACD and RSI. If the MACD histogram is above zero and the MACD line crosses below the signal line, the strategy exits the trade. Additionally, if the RSI had previously shown the market reaching overbought levels, it also triggers a position close. Together, these conditions imply that the strategy closes out long positions when the price may have peaked and upward momentum is waning.
Overall, by combining the signals provided by the MACD and RSI, the strategy aims to open positions as soon as a trend shows early signs of reversing and close positions when the trend may be ending, thus optimizing entry and exit points to enhance overall trading performance.
To mitigate these risks, one can consider introducing other leading indicators as filters, optimizing parameters to suit different market conditions, and setting appropriate stop-losses and take-profits to manage risk on individual trades.
By implementing these optimization measures, the risk-adjusted returns of the strategy can be further enhanced, making it better suited to navigate the ever-changing market environment.
Snehashish’s long-term trading strategy skillfully combines the MACD and RSI technical indicators to capture market turning points with greater precision, optimizing entry and exit timings. By waiting for the RSI to confirm an oversold state and using the MACD line crossing the signal line as an entry signal, the strategy can enter positions as soon as a trend shows early signs of reversing. Similarly, by utilizing the relative positions of the MACD histogram and signal line, along with the RSI’s overbought signal, the strategy can exit positions in a timely manner when an uptrend may be ending.
Although the strategy shows good potential, it still carries some risks, such as overtrading in choppy markets and signal lag during strong trends. To mitigate these risks, one can consider introducing other indicators, optimizing parameter settings, enhancing market environment analysis, and improving position sizing, among other measures.
Overall, this MACD and RSI-based long-term trading strategy provides investors with a reliable framework for capturing market turning points and optimizing entry and exit timings. With further optimization and refinement, the strategy could become a powerful tool for investors to achieve robust long-term returns in the face of changing market conditions.
/*backtest start: 2024-03-01 00:00:00 end: 2024-03-31 23:59:59 period: 1h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=5 // snehashish 2024 strategy(title='spl Long Strategy', initial_capital=100000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, pyramiding=0, currency='USD', overlay=true) //// Stoploss and Take Profit Parameters // Enable Long Strategy enable_long_strategy = input.bool(true, title='Enable Long Strategy', group='SL/TP For Long Strategy', inline='1') long_stoploss_value = input.float(50, title='Stoploss %', minval=0, group='SL/TP For Long Strategy', inline='2') long_takeprofit_value = input.float(50, title='Take Profit %', minval=0, group='SL/TP For Long Strategy', inline='2') // Enable Short Strategy enable_short_strategy = input.bool(true, title='Enable Short Strategy', group='SL/TP For Short Strategy', inline='3') short_stoploss_value = input.float(50, title='Stoploss %', minval=0, group='SL/TP For Short Strategy', inline='4') short_takeprofit_value = input.float(50, title='Take Profit %', minval=0, group='SL/TP For Short Strategy', inline='4') // Date Range start_date = input.int(1, title='Start Date', minval=1, maxval=31, group='Date Range', inline='1') start_month = input.int(1, title='Start Month', minval=1, maxval=12, group='Date Range', inline='2') start_year = input.int(2023, title='Start Year', minval=1800, maxval=3000, group='Date Range', inline='3') end_date = input.int(1, title='End Date', minval=1, maxval=31, group='Date Range', inline='4') end_month = input.int(12, title='End Month', minval=1, maxval=12, group='Date Range', inline='5') end_year = input.int(2077, title='End Year', minval=1800, maxval=3000, group='Date Range', inline='6') in_date_range = true //// Indicator Inputs // RSI rsi_over_sold = input.int(30, title='Over Sold Level', group='RSI') rsi_over_bought = input.int(70, title='Over Bought Level', group='RSI') rsi_length = input.int(14, title='RSI Length', group='RSI') rsi = ta.rsi(close, rsi_length) // MACD fast_ma = input.int(12, title='FastMA Length', group='MACD') slow_ma = input.int(26, title='SlowMA Length', group='MACD') signal_length = input.int(9, title='Signal Length', group='MACD') [macd_line, signal_line, _] = ta.macd(close, fast_ma, slow_ma, signal_length) //// Strategy Logic was_over_sold = ta.barssince(rsi <= rsi_over_sold) <= 10 was_over_bought = ta.barssince(rsi >= rsi_over_bought) <= 10 crossover_bull = ta.crossover(macd_line, signal_line) crossover_bear = ta.crossunder(macd_line, signal_line) buy_signal = was_over_sold and crossover_bull and in_date_range sell_signal = was_over_bought and crossover_bear and in_date_range // Long Strategy if (enable_long_strategy and buy_signal) strategy.entry('Long', strategy.long) strategy.exit('Long SL/TP', from_entry='Long', stop=strategy.position_avg_price * (1 - long_stoploss_value / 100), limit=strategy.position_avg_price * (1 + long_takeprofit_value / 100)) // Short Strategy if (enable_short_strategy and sell_signal) strategy.entry('Short', strategy.short) strategy.exit('Short SL/TP', from_entry='Short', stop=strategy.position_avg_price * (1 + short_stoploss_value / 100), limit=strategy.position_avg_price * (1 - short_takeprofit_value / 100))template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6