SMA cross + TokIO check

//@version=6
// Demo: a long-only SMA crossover with the TokIO check pasted underneath.
// On SPY daily it looks significant alone; set "Variants tried" to the 20
// parameter sets you might have tried and it isn't.
// To use with your own strategy, paste pine/tokio_check.pine below your code.
strategy("SMA cross + TokIO check", overlay = true, initial_capital = 10000, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, commission_type = strategy.commission.percent, commission_value = 0.05)
fastLen = input.int(10, "Fast SMA")
slowLen = input.int(30, "Slow SMA")
f = ta.sma(close, fastLen)
s = ta.sma(close, slowLen)
plot(f, color = color.teal)
plot(s, color = color.orange)
if ta.crossover(f, s)
    strategy.entry("Long", strategy.long)
if ta.crossunder(f, s)
    strategy.close("Long")

// ─────────────────────────────────────────────────────────────────────────────
// TokIO backtest check for TradingView — paste below your strategy's code.
// https://github.com/Kuzi-DEV/Tokio-ai   (MIT licence)
//
// Is this strategy's profit distinguishable from luck? Reads the strategy's
// closed trades and draws a table on the chart with:
//   • a one-sided test of the mean trade return that allows for serial
//     dependence (Bartlett HAC, Andrews bandwidth, Kiefer-Vogelsang fixed-b),
//     the same test as TokIO's Python check_backtest on a trade list
//   • Sidak's correction for the number of variants you tried ("Trials")
//   • the Probabilistic / Deflated Sharpe Ratio (Bailey & Lopez de Prado),
//     as published and with the dependence correction
//   • the minimum track record length, and a symmetric-trim check
//
// A trade's return is its P&L as a % of the position's value
// (strategy.closedtrades.profit_percent), so position sizing doesn't move
// the verdict. The Python package does more — several variants corrected
// for each other, probability of backtest overfitting, costs/breakeven, the
// lookahead (one-bar delay) check — on the Strategy Tester's trade export:
//     pip install tokio-ai
//     tokio-ai-backtest "List of trades.csv" --trials 30
//
// Requires: //@version=6 and a strategy() script. Rename nothing below.
// ─────────────────────────────────────────────────────────────────────────────

tokio_trials = input.int(1, "Variants tried (incl. this one)", minval = 1, group = "TokIO check",
     tooltip = "Every parameter set, indicator or rule you tried before keeping this one. Be honest: the correction is only as good as this number.")
tokio_alpha = input.float(0.05, "Significance level", minval = 0.001, maxval = 0.5, group = "TokIO check")
tokio_pos = input.string("bottom_right", "Table position", options = ["top_left", "top_center", "top_right", "middle_left", "middle_center", "middle_right", "bottom_left", "bottom_center", "bottom_right"], group = "TokIO check")

// Standard normal CDF (Abramowitz & Stegun 7.1.26 erf, |error| < 1.5e-7)
tokio_cdf(float tk_z) =>
    tk_x = math.abs(tk_z) / math.sqrt(2.0)
    tk_t = 1.0 / (1.0 + 0.3275911 * tk_x)
    tk_y = 1.0 - (((((1.061405429 * tk_t - 1.453152027) * tk_t) + 1.421413741) * tk_t - 0.284496736) * tk_t + 0.254829592) * tk_t * math.exp(-tk_x * tk_x)
    tk_z >= 0 ? 0.5 * (1.0 + tk_y) : 0.5 * (1.0 - tk_y)

// Inverse standard normal CDF (Acklam, relative error < 1.2e-9)
tokio_inv(float tk_p) =>
    tk_a1 = -3.969683028665376e+01
    tk_a2 = 2.209460984245205e+02
    tk_a3 = -2.759285104469687e+02
    tk_a4 = 1.383577518672690e+02
    tk_a5 = -3.066479806614716e+01
    tk_a6 = 2.506628277459239e+00
    tk_b1 = -5.447609879822406e+01
    tk_b2 = 1.615858368580409e+02
    tk_b3 = -1.556989798598866e+02
    tk_b4 = 6.680131188771972e+01
    tk_b5 = -1.328068155288572e+01
    tk_c1 = -7.784894002430293e-03
    tk_c2 = -3.223964580411365e-01
    tk_c3 = -2.400758277161838e+00
    tk_c4 = -2.549732539343734e+00
    tk_c5 = 4.374664141464968e+00
    tk_c6 = 2.938163982698783e+00
    tk_d1 = 7.784695709041462e-03
    tk_d2 = 3.224671290700398e-01
    tk_d3 = 2.445134137142996e+00
    tk_d4 = 3.754408661907416e+00
    float tk_r = na
    if tk_p < 0.02425
        tk_q = math.sqrt(-2.0 * math.log(tk_p))
        tk_r := (((((tk_c1 * tk_q + tk_c2) * tk_q + tk_c3) * tk_q + tk_c4) * tk_q + tk_c5) * tk_q + tk_c6) / ((((tk_d1 * tk_q + tk_d2) * tk_q + tk_d3) * tk_q + tk_d4) * tk_q + 1.0)
    else if tk_p <= 1.0 - 0.02425
        tk_q = tk_p - 0.5
        tk_s = tk_q * tk_q
        tk_r := (((((tk_a1 * tk_s + tk_a2) * tk_s + tk_a3) * tk_s + tk_a4) * tk_s + tk_a5) * tk_s + tk_a6) * tk_q / (((((tk_b1 * tk_s + tk_b2) * tk_s + tk_b3) * tk_s + tk_b4) * tk_s + tk_b5) * tk_s + 1.0)
    else
        tk_q = math.sqrt(-2.0 * math.log(1.0 - tk_p))
        tk_r := -(((((tk_c1 * tk_q + tk_c2) * tk_q + tk_c3) * tk_q + tk_c4) * tk_q + tk_c5) * tk_q + tk_c6) / ((((tk_d1 * tk_q + tk_d2) * tk_q + tk_d3) * tk_q + tk_d4) * tk_q + 1.0)
    tk_r

tokio_fmt(float tk_v, int dp) =>
    na(tk_v) ? "–" : str.tostring(tk_v, dp == 2 ? "#,##0.00" : dp == 3 ? "0.000" : "0.0000")

if barstate.islastconfirmedhistory or (barstate.islast and barstate.isrealtime)
    int tk_n = strategy.closedtrades
    var table tk_tbl = na
    if not na(tk_tbl)
        table.delete(tk_tbl)
    tk_pos = switch tokio_pos
        "top_left" => position.top_left
        "top_center" => position.top_center
        "top_right" => position.top_right
        "middle_left" => position.middle_left
        "middle_center" => position.middle_center
        "middle_right" => position.middle_right
        "bottom_left" => position.bottom_left
        "bottom_center" => position.bottom_center
        => position.bottom_right
    tk_tbl := table.new(tk_pos, 2, 12, bgcolor = color.new(color.black, 0), border_color = color.gray, border_width = 1, frame_color = color.gray, frame_width = 1)
    tk_txt = color.white
    table.cell(tk_tbl, 0, 0, "TokIO check", text_color = tk_txt, text_size = size.small)
    if tk_n < 60
        table.cell(tk_tbl, 1, 0, "NOT REPORTABLE", text_color = color.orange, text_size = size.small)
        table.cell(tk_tbl, 0, 1, "Closed trades", text_color = tk_txt, text_size = size.small)
        table.cell(tk_tbl, 1, 1, str.tostring(tk_n) + " (need 60+)", text_color = tk_txt, text_size = size.small)
    else
        tk_rets = array.new_float(0)
        for tk_i = 0 to tk_n - 1
            array.push(tk_rets, strategy.closedtrades.profit_percent(tk_i) / 100.0)
        float tk_m = array.avg(tk_rets)
        // central moments (population), Bartlett long-run variance
        float tk_s2 = 0.0
        float tk_s3 = 0.0
        float tk_s4 = 0.0
        float tk_g1 = 0.0
        for tk_i = 0 to tk_n - 1
            tk_d = array.get(tk_rets, tk_i) - tk_m
            tk_s2 += tk_d * tk_d
            tk_s3 += tk_d * tk_d * tk_d
            tk_s4 += tk_d * tk_d * tk_d * tk_d
            if tk_i > 0
                tk_g1 += tk_d * (array.get(tk_rets, tk_i - 1) - tk_m)
        tk_v = tk_s2 / tk_n
        tk_sd = math.sqrt(tk_s2 / (tk_n - 1))
        tk_skew = (tk_s3 / tk_n) / math.pow(tk_v, 1.5)
        tk_kurt = (tk_s4 / tk_n) / (tk_v * tk_v)
        // Andrews (1991) AR(1) plug-in and the Newey-West rule; the larger wins
        tk_rho = math.max(-0.97, math.min(0.97, tk_g1 / tk_s2))
        tk_a1 = 4.0 * tk_rho * tk_rho / (math.pow(1.0 - tk_rho, 2) * math.pow(1.0 + tk_rho, 2))
        int tk_L = math.min(tk_n - 1, math.max(int(math.floor(4.0 * math.pow(tk_n / 100.0, 2.0 / 9.0))), int(1.1447 * math.pow(tk_a1 * tk_n, 1.0 / 3.0))))
        float tk_lrv = tk_s2
        if tk_L > 0
            for tk_l = 1 to tk_L
                float tk_gl = 0.0
                for tk_t = tk_l to tk_n - 1
                    tk_gl += (array.get(tk_rets, tk_t) - tk_m) * (array.get(tk_rets, tk_t - tk_l) - tk_m)
                tk_lrv += 2.0 * (1.0 - tk_l / (tk_L + 1.0)) * tk_gl
        tk_lrv := math.max(tk_lrv / tk_n, 1e-18)
        tk_b = (tk_L + 1.0) / tk_n
        tk_fb = 1.6449 / (1.6449 + 2.1859 * tk_b + 0.3142 * tk_b * tk_b - 0.3427 * tk_b * tk_b * tk_b)
        tk_z = tk_m / math.sqrt(tk_lrv / tk_n) * tk_fb
        tk_p_alone = 1.0 - tokio_cdf(tk_z)
        tk_p_adj = 1.0 - math.pow(1.0 - tk_p_alone, tokio_trials)
        tk_z_naive = tk_m / (tk_sd / math.sqrt(tk_n))
        tk_p_naive = 1.0 - tokio_cdf(tk_z_naive)
        // Sharpe per trade; annualized by trades per year
        tk_sr = tk_m / tk_sd
        tk_span_years = (strategy.closedtrades.exit_time(tk_n - 1) - strategy.closedtrades.entry_time(0)) / (365.25 * 86400000.0)
        tk_tpy = tk_span_years > 0 ? tk_n / tk_span_years : na
        tk_sr_ann = na(tk_tpy) ? na : tk_sr * math.sqrt(tk_tpy)
        // PSR / DSR (Bailey & Lopez de Prado); vr scales Var(SR) for dependence
        tk_vr = (tk_lrv / tk_v) / (tk_fb * tk_fb)
        tk_core = math.max(1.0 - tk_skew * tk_sr + (tk_kurt - 1.0) / 4.0 * tk_sr * tk_sr, 1e-12)
        tk_gam = 0.5772156649015329
        tk_bench = tokio_trials > 1 ? math.sqrt(1.0 / (tk_n - 1)) * ((1.0 - tk_gam) * tokio_inv(1.0 - 1.0 / tokio_trials) + tk_gam * tokio_inv(1.0 - 1.0 / (tokio_trials * math.e))) : 0.0
        tk_psr_pub = tokio_cdf((tk_sr - tk_bench) * math.sqrt(tk_n - 1) / math.sqrt(tk_core))
        tk_psr_dep = tokio_cdf((tk_sr - tk_bench) * math.sqrt(tk_n - 1) / math.sqrt(tk_core * tk_vr))
        tk_mintrl = tk_sr > tk_bench ? 1.0 + tk_core * tk_vr * math.pow(tokio_inv(0.95) / (tk_sr - tk_bench), 2) : na
        // symmetric trim: drop the 5 best and 5 worst trades
        tk_sorted = array.copy(tk_rets)
        array.sort(tk_sorted, order.ascending)
        tk_k = math.min(5, int(tk_n / 20))
        tk_tr = array.slice(tk_sorted, tk_k, tk_n - tk_k)
        tk_sr_trim = array.stdev(tk_tr, false) > 0 ? array.avg(tk_tr) / array.stdev(tk_tr, false) * (na(tk_tpy) ? 1.0 : math.sqrt(tk_tpy)) : na
        tk_sig = tk_p_adj <= tokio_alpha
        tk_verdict = tk_sig ? "SIGNIFICANT" : "NOT SIGNIFICANT"
        table.cell(tk_tbl, 1, 0, tk_verdict + (tokio_trials > 1 ? " (" + str.tostring(tokio_trials) + " trials)" : ""), text_color = tk_sig ? color.lime : color.red, text_size = size.small)
        tk_rows = array.from("p-value, corrected", "p-value, this alone", "p-value, plain t-test", "Sharpe (annualized)", (tokio_trials > 1 ? "Deflated" : "Probabilistic") + " Sharpe Ratio", "  as published (indep. trades)", "Min. track record (trades)", "Sharpe w/o 5 best & 5 worst", "Closed trades", "Trades per year")
        tk_vals = array.from(tokio_fmt(tk_p_adj, 4), tokio_fmt(tk_p_alone, 4), tokio_fmt(tk_p_naive, 4), tokio_fmt(tk_sr_ann, 2), tokio_fmt(tk_psr_dep, 3) + "  (0.95 passes)", tokio_fmt(tk_psr_pub, 3), na(tk_mintrl) ? "never" : str.tostring(math.ceil(tk_mintrl)), tokio_fmt(tk_sr_trim, 2), str.tostring(tk_n), tokio_fmt(tk_tpy, 2))
        for tk_i = 0 to array.size(tk_rows) - 1
            table.cell(tk_tbl, 0, tk_i + 1, array.get(tk_rows, tk_i), text_color = tk_txt, text_size = size.small, text_halign = text.align_left)
            table.cell(tk_tbl, 1, tk_i + 1, array.get(tk_vals, tk_i), text_color = tk_txt, text_size = size.small, text_halign = text.align_right)
        table.cell(tk_tbl, 0, 11, "Mean trade return " + tokio_fmt(tk_m * 100.0, 3) + "%; HAC lag window " + str.tostring(tk_L), text_color = color.gray, text_size = size.tiny, text_halign = text.align_left)
        table.merge_cells(tk_tbl, 0, 11, 1, 11)