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The Stochastic Momentum Strategy is a quantitative trading strategy that combines the Stochastic Momentum Index (SMI) and the Relative Strength Index (RSI). It uses the SMI to identify overbought and oversold areas in the market, with the fast RSI acting as a signal filter. It also implements a body filter for more reliable signal selection.

The Stochastic Momentum Index (SMI) is a common technical indicator used in quantitative trading that combines the strengths of momentum and oscillation indicators.

Specifically, the SMI is calculated as:

SMI = (Close - (HH + LL)/2)/(0.5*(HH - LL)) * 100

where HH is the highest price over the past N days, and LL is the lowest price.

So the SMI incorporates both the trend-following judgment of momentum and the reversal judgment of oscillation. Values above 80 are considered overbought, while values below 20 are oversold. The strategy generates trading signals when SMI reaches these overbought or oversold levels.

The Relative Strength Index (RSI) is a standard overbought/oversold indicator. This strategy uses a fast RSI with a period of 7 to judge short-term overbought/oversold conditions.

Readings below 20 are considered oversold, while those above 80 are deemed overbought per the fast RSI. Signals are generated when these thresholds are breached.

The strategy also implements a body filter by checking the candlestick body size to filter certain signals. Only bodies exceeding a set threshold will trigger trades.

This filters out some false signals and increases reliability.

This approach combines the SMI, fast RSI, and body filter into a robust 3-part system. Using multiple integrated signals improves accuracy and enhances stability.

Both SMI and fast RSI are excellent for detecting exhausted trends. By trading mean-reversions from these overextended areas, the strategy adheres to buying low and selling high.

The ability to both buy dips and short rallies maximizes opportunities across market conditions.

The body filter avoids whipsaws by rejecting low-conviction signals in choppy conditions.

Frequent long/short switching brings whipsaw risk. Optimizing logic could minimize this.

Signals may cluster market participants and spur quick reversals upon entry. Fine-tuning parameters could reduce herding risk.

Extreme events can upend all models. Intelligent stop losses are necessary to control systematic risks.

Testing different SMI/RSI periods and body filter thresholds could uncover optimal values for higher returns.

Incorporating volatility-based or ATR stops would better contain position and portfolio risk.

Models predicting future indicator levels could identify turning points earlier. This would enhance predictive power.

In summary, by integrating the SMI, fast RSI, and body filter, this strategy has created a fairly comprehensive overbought/oversold system. The multi-signal approach improves accuracy, while two-way trade capability and risk controls contribute to balance. With continual parameter and model optimization, it shows promise for capturing gains over the long run.

/*backtest start: 2023-12-22 00:00:00 end: 2024-01-21 00:00:00 period: 1h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //Noro //2018 //@version=2 strategy(title = "Noro's Stochastic Strategy v1.1", shorttitle = "Stochastic str 1.1", overlay = false, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0) //Settings needlong = input(true, defval = true, title = "Long") needshort = input(true, defval = true, title = "Short") usemar = input(false, defval = false, title = "Use Martingale") capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Capital, %") usesmi = input(true, defval = true, title = "Use SMI Strategy") usersi = input(true, defval = true, title = "Use RSI Strategy") usebod = input(true, defval = true, title = "Use Body-Filter") a = input(5, "SMI Percent K Length") b = input(3, "SMI Percent D Length") limit = input(50, defval = 50, minval = 1, maxval = 100, title = "SMI Limit") fromyear = input(2017, defval = 2017, minval = 1900, maxval = 2100, title = "From Year") toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year") frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month") tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month") fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day") today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day") //Fast RSI fastup = rma(max(change(close), 0), 7) fastdown = rma(-min(change(close), 0), 7) fastrsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown)) //Stochastic Momentum Index ll = lowest (low, a) hh = highest (high, a) diff = hh - ll rdiff = close - (hh+ll)/2 avgrel = ema(ema(rdiff,b),b) avgdiff = ema(ema(diff,b),b) SMI = avgdiff != 0 ? (avgrel/(avgdiff/2)*100) : 0 SMIsignal = ema(SMI,b) //Lines plot(SMI, color = blue, linewidth = 3, title = "Stochastic Momentum Index") plot(SMIsignal, color = red, linewidth = 3, title = "SMI Signal Line") plot(limit, color = black, title = "Over Bought") plot(-1 * limit, color = black, title = "Over Sold") plot(0, color = blue, title = "Zero Line") //Body Filter nbody = abs(close - open) abody = sma(nbody, 10) body = nbody > abody / 3 or usebod == false //Signals up1 = SMIsignal < -1 * limit and close < open and body and usesmi dn1 = SMIsignal > limit and close > open and body and usesmi up2 = fastrsi < 20 and close < open and body and usersi dn2 = fastrsi > 80 and close > open and body and usersi exit = ((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body //Trading profit = exit ? ((strategy.position_size > 0 and close > strategy.position_avg_price) or (strategy.position_size < 0 and close < strategy.position_avg_price)) ? 1 : -1 : profit[1] mult = usemar ? exit ? profit == -1 ? mult[1] * 2 : 1 : mult[1] : 1 lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 * mult : lot[1] if up1 or up2 if strategy.position_size < 0 strategy.close_all() strategy.entry("long", strategy.long, needlong == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59))) if dn1 or dn2 if strategy.position_size > 0 strategy.close_all() strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59))) if time > timestamp(toyear, tomonth, today, 23, 59) or exit strategy.close_all()template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6