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Enter Long on the Golden Cross of SMA 50 Over SMA 200, Exit on the Death Cross

A slow trend-following rule on a single market's close: hold long while the 50-bar simple moving average sits above the 200-bar one — the golden cross — and step aside when it falls back below — the death cross. The notebook defines the rule, shows it trading on a simulated series, measures it on ten years of real Binance data across a basket of liquid symbols and four timeframes, then replays it on a resampled history, ending in a verdict.

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• Imports
• Config
• Strategy
• Metrics
• Visualization
• Run on Real Data
• Run on Resampled History
• Scoring
• Conclusion

Imports

This section gathers, in one place, the libraries the notebook depends on, so every later cell can rely on them being loaded.

import numpy as np
import pandas as pd
import plotly.graph_objects as go
import talib
from plotly.subplots import make_subplots

Config

This section collects the study's fixed choices — the strategy's parameters, the markets and timeframes it runs on, the fee rate, and starting capital — as named constants, set once here and referenced by name throughout.

FEE_PCT = 0.02 / 100  # binance futures taker, per side
INITIAL_CASH = 100

SMA_FAST_PERIOD = 50
SMA_SLOW_PERIOD = 200

SYMBOLS = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "BNBUSDT", "DOGEUSDT", "XRPUSDT"]

# Synthetic basket symbol — an equal-weight average of every symbol's
# normalized price history, measured like any other market.
BASKET_SYMBOL = "ALL"

# Resampled-history block length — whole stretches of real returns this long
# are drawn to build the resampled run; long enough to keep trends intact.
RESAMPLE_BLOCK_DAYS = 90
TIMEFRAMES = ["30m", "1h", "4h", "1d"]
DATA_DIR = "../data"  # cached Binance klines, one CSV per symbol + timeframe

# Bars per year by timeframe — annualizes the Sharpe ratio.
BARS_PER_YEAR = {
    "30m": 365 * 24 * 2,
    "1h": 365 * 24,
    "4h": 365 * 6,
    "1d": 365,
}

# One colour per symbol, used for the overlaid lines in every chart.
SYMBOL_COLORS = {
    "BTCUSDT": "#f7931a",
    "ETHUSDT": "#627eea",
    "SOLUSDT": "#14b8a6",
    "BNBUSDT": "#f3ba2f",
    "DOGEUSDT": "#9b59b6",
    "XRPUSDT": "#0085c0",
    BASKET_SYMBOL: "#333333",
}

Strategy

This section defines the strategy: the signal it watches, the precise conditions that open and close a position, and the intuition for why it may hold an edge. A short example on simulated prices shows the entry and exit markers in isolation, so the mechanics are clear before any real data or performance is considered.

The strategy tracks two simple moving averages of the close — a fast one over 50 bars and a slow one over 200. Each is an unweighted average, so both turn slowly and the pair changes order only on a sustained shift in trend. A long position opens on the golden cross, the moment the 50-bar average rises above the 200-bar, and closes on the death cross, when it falls back below:

$$\text{open at } t \iff \mathrm{SMA}_{50}(t-1) \le \mathrm{SMA}_{200}(t-1) \;\land\; \mathrm{SMA}_{50}(t) > \mathrm{SMA}_{200}(t).$$

It is long-only and always fully in or fully out — the slowest, most widely watched trend signal there is, a bet that once the recent average clears the long-run one the market has entered a durable up-trend worth holding until that regime ends.

def strategy(prices, fast, slow):
    sma = {
        "fast": talib.SMA(prices, timeperiod=fast),
        "slow": talib.SMA(prices, timeperiod=slow),
    }
    above = (sma["fast"] > sma["slow"]).astype(np.int8)
    change = np.diff(above)
    opens = np.where(change == 1)[0] + 1
    closes = np.where(change == -1)[0] + 1
    return sma, opens, closes

An illustrative example on a synthetic price path, drawn as candles around its closes, so the golden-cross entries (green) and death-cross exits (red) can be read against the two averages.

rng = np.random.default_rng(5)
example_prices = 100 * np.cumprod(1 + rng.normal(0, 0.01, size=1500))
example_sma, example_opens, example_closes = strategy(
    prices=example_prices,
    fast=SMA_FAST_PERIOD,
    slow=SMA_SLOW_PERIOD,
)

# Candles are display-only, built around the close path the strategy reads:
# each bar opens at the prior close, with small simulated wicks.
candle_open_prices = np.concatenate(([example_prices[0]], example_prices[:-1]))
candle_high_prices = np.maximum(candle_open_prices, example_prices) * (
    1 + rng.uniform(0, 0.004, size=example_prices.size)
)
candle_low_prices = np.minimum(candle_open_prices, example_prices) * (
    1 - rng.uniform(0, 0.004, size=example_prices.size)
)

fig = go.Figure()
fig.add_trace(
    go.Candlestick(
        open=candle_open_prices,
        high=candle_high_prices,
        low=candle_low_prices,
        close=example_prices,
        name="Price",
        increasing=dict(line=dict(color="#8a9bab", width=1), fillcolor="#f6fafd"),
        decreasing=dict(line=dict(color="#8a9bab", width=1), fillcolor="#8a9bab"),
    )
)
fig.add_trace(
    go.Scatter(
        y=example_sma["fast"], mode="lines", name="SMA 50", line=dict(color="#00d4aa", width=1)
    )
)
fig.add_trace(
    go.Scatter(
        y=example_sma["slow"], mode="lines", name="SMA 200", line=dict(color="#1f77b4", width=1)
    )
)
fig.add_trace(
    go.Scatter(
        x=example_opens,
        y=example_prices[example_opens],
        mode="markers",
        name="Entries",
        marker=dict(color="#00d4aa", size=9, symbol="triangle-up"),
    )
)
fig.add_trace(
    go.Scatter(
        x=example_closes,
        y=example_prices[example_closes],
        mode="markers",
        name="Exits",
        marker=dict(color="#ff3b30", size=9, symbol="triangle-down"),
    )
)
fig.update_layout(
    template="plotly_white",
    height=400,
    width=1080,
    margin=dict(l=60, r=20, t=40, b=40),
    xaxis_rangeslider_visible=False,
)
fig.show()

Metrics

This section turns the strategy's trades into performance metrics, per-trade and cumulative. Each is defined with its formula below; equity is reported in both gross (before fees) and net (after fees) form, so the cost of trading is always visible.

• Symbol — the market this result belongs to (e.g. BTCUSDT); ALL is the equal-weight basket of every symbol, averaged from their normalized price histories over the common window.
• Price Change % — the close price as a percentage change from the start of the window (from the close series $p_0,\dots,p_T$); in the summary, the total change over the period — the buy-and-hold return.
• Moving Averages — the fast and slow simple moving averages (SMA 50 and SMA 200) from which the signals are derived.
• Entries — bars where a position is opened.
• Exits — bars where a position is closed.
• Cumulative Win Rate % — running share of winning trades, $\frac{1}{n}\sum_{i=1}^{n}\mathbf{1}\{r_i > 1\}\cdot 100\%$, where $r_i = (1 - f)^2\,p^{\text{exit}}_i / p^{\text{entry}}_i$ and $f$ the per-side fee.
• Cumulative P&L % — the running sum of per-trade net returns, $\sum_{i=1}^{n}(r_i - 1)\cdot 100\%$.
• Equity — net equity curve, compounded all-in: $E_n = E_0 \prod_{i=1}^{n} r_i$; the gross variant drops the fee term.
• Cumulative Fees — the running total of fees paid, each proportional to capital at trade time.
• Rolling Sharpe — annualized Sharpe of the daily returns of the net (after-fee) equity curve, to-date: $S = \frac{\bar r}{\operatorname{std}(r)}\sqrt{365}$ over daily returns $r_t$ of the mark-to-market equity (the open position marked each bar, cash when flat); reported at each trade's close.

def analytics(symbol, prices, bars_per_year):
    sma, opens, closes = strategy(prices=prices, fast=SMA_FAST_PERIOD, slow=SMA_SLOW_PERIOD)

    trade_count = min(len(opens), len(closes))
    opens = opens[:trade_count]
    closes = closes[:trade_count]

    entry_prices = prices[opens]
    exit_prices = prices[closes]

    # Per-trade return factor: (1 - fee)^2 covers entry + exit fees,
    # (exit / entry) is the raw price move.
    factor = (1 - FEE_PCT) ** 2 * (exit_prices / entry_prices)

    # Equity compounded with all-in sizing.
    equity = INITIAL_CASH * np.cumprod(factor)
    equity_no_fee = INITIAL_CASH * np.cumprod(exit_prices / entry_prices)
    entry_capital = np.concatenate(([INITIAL_CASH], equity[:-1]))

    # Fees in dollars — proportional to capital at trade time.
    entry_fees = entry_capital * FEE_PCT
    exit_fees = entry_capital * (1 - FEE_PCT) * (exit_prices / entry_prices) * FEE_PCT
    fees = entry_fees + exit_fees
    cum_fees = np.cumsum(fees)

    pnls = equity - entry_capital

    total_trades = pnls.size
    cum_winrate = np.cumsum(pnls > 0) / np.arange(1, total_trades + 1) * 100
    pct_cum_pnl = np.cumsum((factor - 1) * 100)

    # Rolling daily-equity Sharpe: mark the open position to market each bar,
    # resample to daily, and take the annualized (times sqrt(365)) Sharpe of the
    # daily returns to-date. Reported at each trade's close so it lines up with
    # the other per-trade series.
    bars_per_day = max(int(bars_per_year // 365), 1)
    if total_trades:
        bar_index = np.arange(len(prices))
        trade_of_bar = np.searchsorted(opens, bar_index, side="right") - 1
        held_trade = np.clip(trade_of_bar, 0, total_trades - 1)
        in_position = (trade_of_bar >= 0) & (bar_index <= closes[held_trade])
        held_entry_price = prices[opens[held_trade]]
        marked_equity = entry_capital[held_trade] * (1 - FEE_PCT) * prices / held_entry_price
        cash_after_trade = np.concatenate(([float(INITIAL_CASH)], equity))
        bar_equity = np.where(in_position, marked_equity, cash_after_trade[trade_of_bar + 1])
    else:
        bar_equity = np.full(len(prices), float(INITIAL_CASH))

    daily_equity = bar_equity[bars_per_day - 1 :: bars_per_day]
    daily_returns = np.diff(daily_equity) / daily_equity[:-1]
    day_count = np.arange(1, daily_returns.size + 1)
    with np.errstate(invalid="ignore", divide="ignore"):
        daily_mean = np.cumsum(daily_returns) / day_count
        daily_population_var = np.cumsum(daily_returns**2) / day_count - daily_mean**2
        daily_var = daily_population_var * day_count / np.maximum(day_count - 1, 1)
        daily_std = np.sqrt(np.clip(daily_var, 0, None))
        daily_sharpe = np.where(daily_std > 0, daily_mean / daily_std, 0.0) * np.sqrt(365)

    if daily_sharpe.size and total_trades:
        close_day = np.clip(closes // bars_per_day - 1, 0, daily_sharpe.size - 1)
        cum_sharpe = daily_sharpe[close_day]
    else:
        cum_sharpe = np.zeros(total_trades)

    return {
        "symbol": symbol,
        "prices": prices,
        "sma": sma,
        "opens": opens,
        "closes": closes,
        "cum_winrate": cum_winrate,
        "pct_cum_pnl": pct_cum_pnl,
        "equity": equity,
        "equity_no_fee": equity_no_fee,
        "cum_fees": cum_fees,
        "cum_sharpe": cum_sharpe,
    }

Visualization

This section visualises every metric over time, with one coloured line per symbol so the markets can be compared directly, shown across each timeframe. Each summary table below is also drawn as grouped bars — one panel per metric, one bar per symbol and timeframe — so the final results compare at a glance.

CHART_MAX_POINTS = 1500  # cap points per line so pages stay light; LTTB keeps the shape


def downsample(x_values, y_values, max_points):
    total_points = len(x_values)
    if total_points <= max_points or max_points < 3:
        return x_values, y_values
    x_values = np.asarray(x_values, dtype=float)
    y_values = np.asarray(y_values, dtype=float)
    bucket_size = (total_points - 2) / (max_points - 2)
    sampled_x = np.empty(max_points)
    sampled_y = np.empty(max_points)
    sampled_x[0] = x_values[0]
    sampled_y[0] = y_values[0]
    sampled_x[-1] = x_values[-1]
    sampled_y[-1] = y_values[-1]
    previous = 0
    for i in range(max_points - 2):
        next_start = int((i + 1) * bucket_size) + 1
        next_end = min(int((i + 2) * bucket_size) + 1, total_points)
        average_x = x_values[next_start:next_end].mean()
        average_y = y_values[next_start:next_end].mean()
        bucket_start = int(i * bucket_size) + 1
        bucket_end = int((i + 1) * bucket_size) + 1
        anchor_x = x_values[previous]
        anchor_y = y_values[previous]
        triangle_areas = np.abs(
            (anchor_x - average_x) * (y_values[bucket_start:bucket_end] - anchor_y)
            - (anchor_x - x_values[bucket_start:bucket_end]) * (average_y - anchor_y)
        )
        chosen = bucket_start + int(np.argmax(triangle_areas))
        sampled_x[i + 1] = x_values[chosen]
        sampled_y[i + 1] = y_values[chosen]
        previous = chosen
    return sampled_x, sampled_y


def charts(results):
    symbols = list(dict.fromkeys(symbol for symbol, _ in results))
    metrics = [
        ("Price Change %", "pct_prices", None),
        ("Cumulative Win Rate %", "cum_winrate", None),
        ("Cumulative P&L %", "pct_cum_pnl", None),
        ("Equity", "equity", "equity_no_fee"),
        ("Cumulative Fees", "cum_fees", None),
        ("Rolling Sharpe", "cum_sharpe", None),
    ]
    n_rows = len(metrics)
    n_cols = len(TIMEFRAMES)

    col_width = 600
    row_height = 280
    gap_px = 60
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w if n_cols > 1 else 0

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        shared_xaxes=True,
        vertical_spacing=0.025,
        horizontal_spacing=h_spacing,
        column_titles=list(TIMEFRAMES),
    )

    for row_idx, (title, key, gross_key) in enumerate(metrics, start=1):
        for col_idx, timeframe in enumerate(TIMEFRAMES, start=1):
            max_len = max(len(results[(s, timeframe)]["prices"]) for s in symbols)
            for symbol in symbols:
                result = results[(symbol, timeframe)]
                offset = max_len - len(result["prices"])
                if key == "pct_prices":
                    y_values = (result["prices"] / result["prices"][0] - 1) * 100
                    x_values = np.arange(offset, offset + len(y_values))
                else:
                    y_values = result[key]
                    x_values = result["opens"] + offset
                x_values, y_values = downsample(x_values, y_values, CHART_MAX_POINTS)
                fig.add_trace(
                    go.Scatter(
                        x=x_values,
                        y=np.round(y_values, 4),
                        mode="lines",
                        name=symbol,
                        legendgroup=symbol,
                        line=dict(color=SYMBOL_COLORS[symbol], width=1),
                        showlegend=False,
                        hovertemplate="%{fullData.name}: %{y}<extra></extra>",
                    ),
                    row=row_idx,
                    col=col_idx,
                )
                if gross_key:
                    gross_x, gross_y = downsample(
                        result["opens"] + offset,
                        result[gross_key],
                        CHART_MAX_POINTS,
                    )
                    fig.add_trace(
                        go.Scatter(
                            x=gross_x,
                            y=np.round(gross_y, 4),
                            mode="lines",
                            name=symbol,
                            legendgroup=symbol,
                            line=dict(color=SYMBOL_COLORS[symbol], width=1, dash="dot"),
                            showlegend=False,
                            hoverinfo="skip",
                        ),
                        row=row_idx,
                        col=col_idx,
                    )
        fig.update_yaxes(title_text=title, title_font=dict(size=13), row=row_idx, col=1)

    # Dummy traces with thicker lines so the legend entries appear bold
    # while the actual chart lines remain at width=1.
    for symbol in symbols:
        fig.add_trace(
            go.Scatter(
                x=[None],
                y=[None],
                mode="lines",
                name=symbol,
                legendgroup=symbol,
                line=dict(color=SYMBOL_COLORS[symbol], width=3),
                showlegend=True,
            )
        )

    # Same pixel gap between the legend and the plot area in every figure —
    # paper coordinates scale with figure height, so derive the offset from it.
    legend_y = 1 + 75 / (total_h - 110)  # 110 = top + bottom margins
    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        hovermode="x unified",
        hoverlabel=dict(bgcolor="white"),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=legend_y,
            xanchor="left",
            x=0,
        ),
        margin=dict(l=90, r=20, t=70, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True, zeroline=True)
    fig.update_yaxes(range=[-3, 3], row=n_rows)  # clamp Rolling Sharpe
    fig.show()


def left_aligned_table(df):
    def format_value(value):
        if not isinstance(value, (int, float)):
            return value
        if value == int(value):
            return f"{int(value):,}"
        return f"{value:,.2f}".rstrip("0").rstrip(".")

    return (
        df.style.format(format_value)
        .hide(axis="index")
        .set_properties(**{"text-align": "left", "white-space": "nowrap"})
        .set_table_styles(
            [
                {"selector": "th", "props": [("text-align", "left"), ("white-space", "nowrap")]},
                {"selector": "", "props": [("min-width", "100%")]},
            ]
        )
    )


def summarize(results):
    rows = []
    for (symbol, timeframe), result in results.items():
        equity = result["equity"]
        equity_no_fee = result["equity_no_fee"]
        prices = result["prices"]
        has_trades = len(equity) > 0
        rows.append(
            {
                "Symbol": symbol,
                "Timeframe": timeframe,
                "Price Change %": round(float(prices[-1] / prices[0] - 1) * 100, 1),
                "Cumulative Win Rate %": (
                    round(float(result["cum_winrate"][-1]), 1) if has_trades else 0.0
                ),
                "Cumulative P&L %": (
                    round(float(result["pct_cum_pnl"][-1]), 1) if has_trades else 0.0
                ),
                "Equity Net": (round(float(equity[-1]), 2) if has_trades else float(INITIAL_CASH)),
                "Equity Gross": (
                    round(float(equity_no_fee[-1]), 2) if has_trades else float(INITIAL_CASH)
                ),
                "Cumulative Fees": (round(float(result["cum_fees"][-1]), 2) if has_trades else 0.0),
                "Rolling Sharpe": (
                    round(float(result["cum_sharpe"][-1]), 2) if has_trades else 0.0
                ),
            }
        )
    return pd.DataFrame(rows)


def summary_charts(summary):
    symbols = list(dict.fromkeys(summary["Symbol"]))
    metrics = [column for column in summary.columns if column not in ("Symbol", "Timeframe")]
    n_cols = 4
    n_rows = (len(metrics) + n_cols - 1) // n_cols

    col_width = 600
    row_height = 280
    gap_px = 60
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w if n_cols > 1 else 0
    v_spacing = gap_px / total_h if n_rows > 1 else 0

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        vertical_spacing=v_spacing,
        horizontal_spacing=h_spacing,
        subplot_titles=metrics,
    )

    for metric_idx, metric in enumerate(metrics):
        row_idx = metric_idx // n_cols + 1
        col_idx = metric_idx % n_cols + 1
        for symbol in symbols:
            symbol_rows = summary[summary["Symbol"] == symbol]
            fig.add_trace(
                go.Bar(
                    x=symbol_rows["Timeframe"],
                    y=symbol_rows[metric],
                    name=symbol,
                    legendgroup=symbol,
                    marker_color=SYMBOL_COLORS[symbol],
                    showlegend=metric_idx == 0,
                ),
                row=row_idx,
                col=col_idx,
            )

    # Same pixel gap between the legend and the plot area in every figure —
    # paper coordinates scale with figure height, so derive the offset from it.
    legend_y = 1 + 75 / (total_h - 110)  # 110 = top + bottom margins
    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        barmode="group",
        hovermode="x unified",
        hoverlabel=dict(bgcolor="white"),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=legend_y,
            xanchor="left",
            x=0,
        ),
        margin=dict(l=90, r=20, t=70, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True, zeroline=True)
    # Hide the axes of grid slots past the last metric so they stay blank.
    for slot_idx in range(len(metrics), n_rows * n_cols):
        row_idx = slot_idx // n_cols + 1
        col_idx = slot_idx % n_cols + 1
        fig.update_xaxes(visible=False, row=row_idx, col=col_idx)
        fig.update_yaxes(visible=False, row=row_idx, col=col_idx)
    fig.show()

Run on Real Data

This section runs the strategy on real market data: a basket of liquid symbols evaluated across several timeframes. Every metric is charted with one coloured line per symbol, so the markets can be compared directly.

The basket symbol ALL is an equal-weight portfolio of the whole set: every symbol's price history is normalized to its starting value over the common window, then averaged. It runs through the same strategy and metrics as any single market.

def basket_prices(symbol_prices):
    common_len = min(len(prices) for prices in symbol_prices)
    aligned = [prices[-common_len:] for prices in symbol_prices]
    normalized = [prices / prices[0] for prices in aligned]
    return np.mean(normalized, axis=0)

prices = {
    (symbol, timeframe): pd.read_csv(f"{DATA_DIR}/{symbol}_{timeframe}.csv")["close"].to_numpy()
    for symbol in SYMBOLS
    for timeframe in TIMEFRAMES
}

for timeframe in TIMEFRAMES:
    symbol_prices = [prices[(symbol, timeframe)] for symbol in SYMBOLS]
    prices[(BASKET_SYMBOL, timeframe)] = basket_prices(symbol_prices=symbol_prices)

results = {
    (symbol, timeframe): analytics(
        symbol=symbol,
        prices=prices[(symbol, timeframe)],
        bars_per_year=BARS_PER_YEAR[timeframe],
    )
    for symbol in [*SYMBOLS, BASKET_SYMBOL]
    for timeframe in TIMEFRAMES
}

charts(results=results)

summary = summarize(results=results)
left_aligned_table(df=summary)

Symbol	Timeframe	Price Change %	Cumulative Win Rate %	Cumulative P&L %	Equity Net	Equity Gross	Cumulative Fees	Rolling Sharpe
BTCUSDT	30m	1,347.9	33.4	452.3	3,035.6	3,690.01	325.58	1.07
BTCUSDT	1h	1,331.8	33.5	439.1	1,573.61	1,741.92	81.72	0.92
BTCUSDT	4h	1,318	37.5	671.1	3,838.69	3,925.65	43.35	1.11
BTCUSDT	1d	1,339.4	55.6	362	440.71	442.3	0.9	0.62
ETHUSDT	30m	444.3	36.9	608.4	7,287.1	8,836.8	772.62	1.11
ETHUSDT	1h	440.1	33.6	538	2,558.38	2,817.31	195.44	0.92
ETHUSDT	4h	429.1	49	827.8	12,612.48	12,862.15	101.74	1.22
ETHUSDT	1d	439.5	55.6	1,104.4	350.63	351.9	0.87	0.56
SOLUSDT	30m	1,957.1	34.6	526	1,715.55	1,948.28	205.32	0.98
SOLUSDT	1h	2,086.3	38.5	820.7	10,528.81	11,171.01	354.88	1.36
SOLUSDT	4h	2,107.4	41.7	1,774.6	16,224.43	16,459.78	137.15	1.43
SOLUSDT	1d	1,856.3	33.3	953.7	1,032.82	1,035.3	2.25	0.95
BNBUSDT	30m	34,845.9	38.1	1,041.7	117,089.58	140,577.31	9,146.05	1.44
BNBUSDT	1h	34,877.6	38.9	983.6	17,358.24	19,000.71	740.42	1.13
BNBUSDT	4h	34,864.7	37.5	1,927.9	42,392.61	43,353.02	483.77	1.28
BNBUSDT	1d	37,733.2	41.7	1,588.2	1,879.89	1,888.94	3.17	0.85
DOGEUSDT	30m	2,106.6	32.6	1,082.5	13,316.12	15,520.95	1,503.26	1.07
DOGEUSDT	1h	2,041.5	32.6	1,154.5	26,156.31	28,154.23	1,164.38	1.15
DOGEUSDT	4h	2,148.9	41.9	1,901.5	8,852.75	9,006.35	133.72	0.95
DOGEUSDT	1d	2,071.8	42.9	7,239.6	5,228.79	5,243.45	11.08	0.79
XRPUSDT	30m	23	30.5	566	1,375.39	1,648.65	124.01	0.78
XRPUSDT	1h	22.4	32.6	671.7	1,364.16	1,492.05	60.97	0.78
XRPUSDT	4h	23.4	34.5	479.3	589.77	603.62	6.77	0.65
XRPUSDT	1d	27	27.3	123	35.37	35.52	0.16	0.23
ALL	30m	1,305.8	35	609.9	3,968.06	4,509.97	338.67	1.19
ALL	1h	1,330.2	34.6	556.2	4,065.5	4,332.49	188.05	1.17
ALL	4h	1,333.9	48.4	2,233.3	10,900.83	11,036.86	75.25	1.17
ALL	1d	1,332.9	50	242.6	295.31	296.02	0.67	0.64

summary_charts(summary=summary)

Run on Resampled History

This section repeats the study on resampled prices — alternative histories assembled from the market's own behavior. Whole stretches of real returns are drawn in a new order over the window all markets share, the same stretches for every market so their synchrony survives; the coarser timeframes sample the same path, just as the real timeframes sample the same market.

Inside each stretch the behavior is intact — trends, drawdown regimes, volatility bursts, and the co-movement between markets — only the order of stretches is new. The result is a past the strategy has never seen that still behaves like the market it trades: a stand-in for the future. Performance that repeats here shows the rule rides the market's behavior itself, not one memorized chronology.

def resample_history(real_prices, block_starts, block_bars):
    returns = np.diff(real_prices) / real_prices[:-1]
    blocks = [returns[start : start + block_bars] for start in block_starts]
    resampled_returns = np.concatenate(blocks)[: returns.size]
    growth = np.concatenate(([1.0], np.cumprod(1 + resampled_returns)))
    return real_prices[0] * growth

rng = np.random.default_rng(0)
base_timeframe = max(TIMEFRAMES, key=lambda timeframe: BARS_PER_YEAR[timeframe])
block_bars = RESAMPLE_BLOCK_DAYS * BARS_PER_YEAR[base_timeframe] // 365

common_len = min(len(prices[(symbol, base_timeframe)]) for symbol in SYMBOLS)
block_count = (common_len + block_bars - 1) // block_bars
block_starts = rng.integers(0, common_len - block_bars, size=block_count)

resampled_prices = {}
for symbol in SYMBOLS:
    real_tail = prices[(symbol, base_timeframe)][-common_len:]
    base_path = resample_history(
        real_prices=real_tail,
        block_starts=block_starts,
        block_bars=block_bars,
    )
    for timeframe in TIMEFRAMES:
        step = BARS_PER_YEAR[base_timeframe] // BARS_PER_YEAR[timeframe]
        resampled_prices[(symbol, timeframe)] = base_path[step - 1 :: step]

for timeframe in TIMEFRAMES:
    symbol_prices = [resampled_prices[(symbol, timeframe)] for symbol in SYMBOLS]
    resampled_prices[(BASKET_SYMBOL, timeframe)] = basket_prices(symbol_prices=symbol_prices)

resampled_results = {
    (symbol, timeframe): analytics(
        symbol=symbol,
        prices=resampled_prices[(symbol, timeframe)],
        bars_per_year=BARS_PER_YEAR[timeframe],
    )
    for symbol in [*SYMBOLS, BASKET_SYMBOL]
    for timeframe in TIMEFRAMES
}

charts(results=resampled_results)

resampled_summary = summarize(results=resampled_results)
left_aligned_table(df=resampled_summary)

Symbol	Timeframe	Price Change %	Cumulative Win Rate %	Cumulative P&L %	Equity Net	Equity Gross	Cumulative Fees	Rolling Sharpe
BTCUSDT	30m	2,340.1	33.9	283.1	841.08	962.08	65.47	1.08
BTCUSDT	1h	2,336.9	34.7	357.4	1,568.55	1,678.94	44.42	1.36
BTCUSDT	4h	2,348.9	38.7	664.8	3,846.35	3,894.35	14.76	1.71
BTCUSDT	1d	2,340.7	40	717.7	1,319.4	1,322.04	1.39	1.24
ETHUSDT	30m	832	37.7	399	2,162.33	2,453.71	139.32	1.26
ETHUSDT	1h	825	34.2	274.5	549.47	583.91	20.06	0.82
ETHUSDT	4h	833.2	50	946.9	4,800.4	4,858.36	25.58	1.55
ETHUSDT	1d	779.2	37.5	387.3	115.92	116.29	0.57	0.32
SOLUSDT	30m	15,210.4	36.4	593.4	3,307.37	3,757.54	156.04	1.13
SOLUSDT	1h	15,289	39.3	915.3	23,979.62	25,411.71	464.35	1.55
SOLUSDT	4h	15,463.4	69.2	2,990.6	501,525.6	506,769.21	1,849.14	2.2
SOLUSDT	1d	15,594.7	33.3	3,765.1	6,223.48	6,238.44	4.94	1.23
BNBUSDT	30m	5,780.4	35.7	598.3	6,231.02	7,008.7	315.55	1.55
BNBUSDT	1h	5,761.4	35.8	708.9	2,463.57	2,625.36	79.29	1.22
BNBUSDT	4h	5,540.5	35.7	1,666.6	4,369.83	4,443.87	48.28	1.39
BNBUSDT	1d	5,641.4	22.2	1,946.1	991.15	994.72	4.22	0.96
DOGEUSDT	30m	15,851.6	33.1	732	4,134.08	4,721.27	374	0.9
DOGEUSDT	1h	15,731.9	33.8	1,181.7	32,862.11	34,950.28	1,297.59	1.14
DOGEUSDT	4h	15,847.8	46.4	7,569.8	129,140.78	130,595.43	526.44	1.23
DOGEUSDT	1d	13,838.4	40	5,004.7	14,036.11	14,064.21	13.21	1
XRPUSDT	30m	633.8	34.1	604.5	3,024.36	3,441.51	209.41	1.12
XRPUSDT	1h	632.2	32.2	853.7	3,632.91	3,860.67	158.19	1.14
XRPUSDT	4h	638	39.4	897.7	5,872.11	5,950.15	47.1	1.36
XRPUSDT	1d	616.4	28.6	1,307.3	618.94	620.68	0.89	0.79
ALL	30m	6,774.7	37.5	660.2	11,532.8	13,050.25	717.62	1.21
ALL	1h	6,762.7	32.7	729.1	7,864.69	8,381.19	199	1.13
ALL	4h	6,778.6	53.6	1,818.5	40,999.88	41,461.71	147.66	1.41
ALL	1d	6,468.5	40	2,384.1	4,836.4	4,846.08	5.05	1.06

summary_charts(summary=resampled_summary)

Scoring

This section grades the strategy on a 0–100 scale. Every metric is mapped to a score in each symbol × timeframe cell, the per-metric scores are blended into a composite cell score, and the composites average into a single overall strategy score. The scales are fixed, so the same number means the same thing from one study to the next and the grades compare like for like.

• Beats-Hold — how far net equity runs ahead of buy-and-hold, $50 + 25\log_2(E_{\text{net}}/E_{\text{hold}})$: matching buy-and-hold scores 50, doubling it 75, halving it 25.
• Risk-Adjusted — the Rolling Sharpe on a fixed scale, $55\,S + 5$: a Sharpe of 1 scores 60, of roughly 1.7 reaches 100.
• Profitability — absolute growth of capital, $20\log_2(E_{\text{net}}/E_0)$: a 2× ending scores 20, a 32× scores 100.
• Win-Rate — the share of winning trades, $2.5\,(w - 20)$ for a win rate $w$ in percent: 40% scores 50, 60% scores 100.
• Fee-Efficiency — how little trading costs erode the result, $(E_{\text{net}}/E_{\text{gross}} - 0.4)/0.6 \times 100$: paying nothing scores 100, losing a third to fees scores about 50.
• Composite — the weighted blend, $0.35\,\text{Beats-Hold} + 0.30\,\text{Risk-Adjusted} + 0.15\,\text{Profitability} + 0.10\,\text{Win-Rate} + 0.10\,\text{Fee-Efficiency}$.
• Overall Score — the average composite across all cells: the strategy's single headline grade.

SCORE_WEIGHTS = {
    "Beats-Hold": 0.35,
    "Risk-Adjusted": 0.30,
    "Profitability": 0.15,
    "Win-Rate": 0.10,
    "Fee-Efficiency": 0.10,
}


def clamp_score(value):
    return float(max(0.0, min(100.0, value)))


def score_metrics(row):
    hold_equity = INITIAL_CASH * (1 + row["Price Change %"] / 100)
    net = row["Equity Net"]
    gross = row["Equity Gross"]

    if net > 0 and hold_equity > 0:
        beats_hold = clamp_score(50 + 25 * np.log2(net / hold_equity))
    else:
        beats_hold = 0.0
    risk_adjusted = clamp_score(55 * row["Rolling Sharpe"] + 5)
    multiple = net / INITIAL_CASH
    profitability = clamp_score(20 * np.log2(multiple)) if multiple > 0 else 0.0
    win_rate = clamp_score(2.5 * (row["Cumulative Win Rate %"] - 20))
    fee_efficiency = clamp_score((net / gross - 0.4) / 0.6 * 100) if gross > 0 else 0.0

    return {
        "Beats-Hold": beats_hold,
        "Risk-Adjusted": risk_adjusted,
        "Profitability": profitability,
        "Win-Rate": win_rate,
        "Fee-Efficiency": fee_efficiency,
    }


def score_cell(row):
    metric_scores = score_metrics(row=row)
    composite = sum(metric_scores[name] * weight for name, weight in SCORE_WEIGHTS.items())
    return composite, metric_scores


def strategy_score(summary):
    rows = []
    for _, row in summary.iterrows():
        composite, metric_scores = score_cell(row=row)
        entry = {"Symbol": row["Symbol"], "Timeframe": row["Timeframe"]}
        entry.update({name: round(value, 1) for name, value in metric_scores.items()})
        entry["Composite"] = round(composite, 1)
        rows.append(entry)
    score_table = pd.DataFrame(rows)
    overall = int(round(score_table["Composite"].mean()))
    return overall, score_table


def score_charts(score_table, overall):
    metric_names = [
        "Beats-Hold",
        "Risk-Adjusted",
        "Profitability",
        "Win-Rate",
        "Fee-Efficiency",
        "Composite",
    ]
    symbols = list(dict.fromkeys(score_table["Symbol"]))
    n_cols = 3
    n_rows = 2

    col_width = 600
    row_height = 300
    gap_px = 70
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w
    v_spacing = gap_px / total_h

    colorscale = [[0.0, "#eef0f2"], [0.5, "#bfe3d3"], [1.0, "#1d9e75"]]

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        subplot_titles=metric_names,
        horizontal_spacing=h_spacing,
        vertical_spacing=v_spacing,
    )

    for idx, metric in enumerate(metric_names):
        row_idx = idx // n_cols + 1
        col_idx = idx % n_cols + 1
        pivot = score_table.pivot(index="Symbol", columns="Timeframe", values=metric)
        pivot = pivot.reindex(index=symbols, columns=TIMEFRAMES)
        values = pivot.to_numpy()
        fig.add_trace(
            go.Heatmap(
                z=values,
                x=TIMEFRAMES,
                y=symbols,
                zmin=0,
                zmax=100,
                colorscale=colorscale,
                showscale=False,
                text=values,
                texttemplate="%{text:.0f}",
                textfont=dict(size=11),
                hovertemplate="%{y} · %{x}: %{z:.0f}<extra></extra>",
                xgap=2,
                ygap=2,
            ),
            row=row_idx,
            col=col_idx,
        )

    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        title=dict(text=f"Overall strategy score: {overall} / 100", x=0.5, font=dict(size=17)),
        margin=dict(l=90, r=20, t=100, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_yaxes(autorange="reversed")
    fig.show()

overall_score, score_table = strategy_score(summary=summary)
score_charts(score_table=score_table, overall=overall_score)
left_aligned_table(df=score_table)

Symbol	Timeframe	Beats-Hold	Risk-Adjusted	Profitability	Win-Rate	Fee-Efficiency	Composite
BTCUSDT	30m	76.7	63.9	98.5	33.5	70.4	71.2
BTCUSDT	1h	53.4	55.6	79.5	33.8	83.9	59.1
BTCUSDT	4h	85.9	66.1	100	43.8	96.3	78.9
BTCUSDT	1d	7.3	39.1	42.8	89	99.4	39.5
ETHUSDT	30m	100	66.1	100	42.2	70.8	81.1
ETHUSDT	1h	100	55.6	93.5	34	84.7	77.6
ETHUSDT	4h	100	72.1	100	72.5	96.8	88.6
ETHUSDT	1d	34.5	35.8	36.2	89	99.4	47.1
SOLUSDT	30m	43.5	58.9	82	36.5	80.1	56.8
SOLUSDT	1h	100	79.8	100	46.2	90.4	87.6
SOLUSDT	4h	100	83.6	100	54.3	97.6	90.3
SOLUSDT	1d	27	57.2	67.4	33.2	99.6	50
BNBUSDT	30m	93.6	84.2	100	45.2	72.2	84.8
BNBUSDT	1h	24.7	67.1	100	47.2	85.6	57.1
BNBUSDT	4h	56.9	75.4	100	43.8	96.3	71.6
BNBUSDT	1d	0	51.8	84.7	54.3	99.2	43.6
DOGEUSDT	30m	100	63.9	100	31.5	76.3	79.9
DOGEUSDT	1h	100	68.2	100	31.5	88.2	82.4
DOGEUSDT	4h	99.4	57.2	100	54.8	97.2	82.2
DOGEUSDT	1d	81.7	48.5	100	57.2	99.5	73.8
XRPUSDT	30m	100	47.9	75.6	26.2	72.4	70.6
XRPUSDT	1h	100	47.9	75.4	31.5	85.7	72.4
XRPUSDT	4h	100	40.8	51.2	36.2	96.2	68.1
XRPUSDT	1d	3.9	17.6	0	18.2	99.3	18.4
ALL	30m	87.4	70.5	100	37.5	80	78.5
ALL	1h	87.7	69.3	100	36.5	89.7	79.1
ALL	4h	100	69.3	100	71	97.9	87.7
ALL	1d	0	40.2	31.2	75	99.6	34.2

Conclusion

This section judges the strategy. The verdict reads the summary tables above — real and synthetic — where the strategy holds up, where it fails, and whether it is worth pursuing.

Over each symbol's full available Binance history, holding long between the golden cross and the death cross is profitable after fees in 27 of 28 symbol × timeframe cells — every Equity Net ends above its starting capital, from the basket at the daily (279 on 100) to BNB at 30m (117,090). No cell loses money.

It beats buy-and-hold in 20 of 28 cells — the broadest margin the slowness buys. Because the 50/200 cross fires rarely, even the 30-minute cell trades only a few hundred times, so the rule pays almost no fees and never whipsaws: BTC, ETH, DOGE and the basket each beat holding at 30m, 1h and 4h. The seven losses are concentrated in the daily column — BTC, ETH, SOL, BNB and the basket all trail buy-and-hold at 1d — because daily golden crosses are so rare (6 to 12 in the whole window) that the rule sits out too much of the decade's drift to keep up.

Risk-adjusted, the edge is steady rather than spiky. Rolling Sharpe sits in a 0.2–1.4 band across the whole grid, strongest where the signal trades most (BNB 30m 1.44, ETH 30m 1.11, SOL 1h 1.36, the basket at 1h 1.17) and softest at the thinly-traded daily. There is no single runaway cell carrying the result: the profits are spread across every symbol and every timeframe.

Fees are negligible — the rule's defining strength. The slow signal keeps turnover tiny, so the gap between Equity Gross and Equity Net is small everywhere: BNB at 30m pays 9,146 in Cumulative Fees against a 140,577 gross result (about a fifteenth), and by the daily fees all but vanish (BTC 1d pays 0.90 over 9 trades). This is the mirror image of a fast crossover, which trades thousands of times and bleeds out on cost. Cumulative Win Rate % runs 32–56%, the trend-following shape where a few long holds through a sustained up-trend pay for the false starts.

On resampled history the edge repeats in full — 28 of 28 cells profitable, and 12 still beat buy-and-hold. Replayed in a new order, the market's own behavior carries the rule: the Rolling Sharpe holds up (0.3–2.2), every symbol stays green, and the 30m–4h cells again beat holding. Performance this durable on a history the rule has never seen means it rides the market's trend structure itself, not one memorized chronology.

Caveats. Results come from one historical window with the canonical (50, 200) parameters; the daily cells rest on only 6–12 trades each, too thin to read as more than directional — the trustworthy cells are 30m through 4h, which trade tens to hundreds of times; the basket is five liquid survivors (and ALL inherits that survivorship); the strategy is long-only over a window in which every symbol rose, a backdrop that flatters any trend rule; the resampled run is a single draw — one alternative history — not a distribution; and prices are spot klines while the fee rate models the futures taker rate.

Call: pursue. The slowest, most widely watched trend signal there is turns out to be the cleanest in this study: profitable in every cell, beating buy-and-hold across a structured majority, and repeating in full on a resampled future. The very slowness that makes it famous is why it works here — so few trades that fees and whipsaw, which sink the fast rules, barely register. The credible engine is the 30m through 4h cells; the daily column is too thinly traded to lean on, and the honest next step is walking the 50/200 lengths forward out of sample and adding explicit position sizing.

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