Top 50 Technical Indicators¶

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Compute 50 trend, momentum, volatility, directional and volume indicators without a TA dependency.

Part 21 of 24 in the ServLoci algo/options trading notebook series — full index in notebooks/README.md.

Setup¶

# Get your dedicated static IPv6 + SOCKS5 credentials free:
#   https://comm.servloci.in/register        (or /auth/google?free=1 for an instant trial)
# Your api_key / api_secret pair shows up in the portal after signup:
#   https://comm.servloci.in/user
!pip install -q "requests[socks]"
!curl -sL https://comm.servloci.in/sdk/servloci.py -o servloci.py

import os
from servloci import ServLoci

SERVLOCI_API_KEY = os.environ.get("SERVLOCI_API_KEY", "dhan:1000000001")   # broker:client_id
SERVLOCI_API_SECRET = os.environ.get("SERVLOCI_API_SECRET", "")            # from the portal — leave blank to run this notebook in demo mode

sl = None
if SERVLOCI_API_SECRET:
    sl = ServLoci(api_key=SERVLOCI_API_KEY, api_secret=SERVLOCI_API_SECRET)
    print("ServLoci configured:", sl.host, sl.port)
else:
    print("SERVLOCI_API_SECRET not set — running in demo mode (no live proxy calls).")

The top 50 used in this course¶

  • Trend (1–11): SMA, EMA, WMA, HMA, DEMA, TEMA, VWMA, MACD, MACD signal, PPO, TRIX.
  • Momentum (12–24): ROC, Momentum, RSI, Stochastic %K/%D, Williams %R, CCI, Ultimate Oscillator, Awesome Oscillator, KST, TSI, Connors RSI, CMO.
  • Volatility/channels (25–36): Bollinger upper/lower/bandwidth, ATR, NATR, True Range, Keltner upper/lower, Donchian upper/lower, standard deviation, historical volatility.
  • Directional/trend state (37–46): ADX, +DI, −DI, Aroon up/down, Vortex +/−, Parabolic SAR, Ichimoku conversion/base.
  • Volume/money flow (47–50): OBV, MFI, CMF, Accumulation/Distribution.

These are features, not buy/sell advice. Parameters are conventional teaching defaults and must be frozen before a fair backtest.

!pip install -q pandas numpy matplotlib
import numpy as np
import pandas as pd

def compute_top_50(frame):
    """Return 50 named indicators from an OHLCV DataFrame.

    Input columns are case-insensitive: open, high, low, close and volume.
    Warm-up rows contain NaN by design; never backfill them into a live signal.
    """
    df = frame.rename(columns={c: str(c).lower() for c in frame.columns}).copy()
    required = {"open", "high", "low", "close", "volume"}
    missing = required.difference(df.columns)
    if missing:
        raise ValueError(f"missing OHLCV columns: {sorted(missing)}")
    o, h, l, c, v = (df[x].astype(float) for x in ("open", "high", "low", "close", "volume"))
    out = pd.DataFrame(index=df.index)
    safe = lambda x: x.replace([np.inf, -np.inf], np.nan)
    ema = lambda x, n: x.ewm(span=n, adjust=False, min_periods=n).mean()
    wma = lambda x, n: x.rolling(n).apply(
        lambda a: np.dot(a, np.arange(1, n + 1)) / (n * (n + 1) / 2), raw=True
    )

    # Trend and moving-average family (1-11)
    out["01_sma_20"] = c.rolling(20).mean()
    out["02_ema_20"] = ema(c, 20)
    out["03_wma_20"] = wma(c, 20)
    out["04_hma_20"] = wma(2 * wma(c, 10) - wma(c, 20), 4)
    e1 = ema(c, 20); e2 = ema(e1, 20); e3 = ema(e2, 20)
    out["05_dema_20"] = 2 * e1 - e2
    out["06_tema_20"] = 3 * e1 - 3 * e2 + e3
    out["07_vwma_20"] = safe((c * v).rolling(20).sum() / v.rolling(20).sum())
    macd = ema(c, 12) - ema(c, 26)
    out["08_macd"] = macd
    out["09_macd_signal"] = ema(macd, 9)
    out["10_ppo"] = safe(100 * macd / ema(c, 26))
    ex1 = ema(c, 15); ex2 = ema(ex1, 15); ex3 = ema(ex2, 15)
    out["11_trix"] = ex3.pct_change(fill_method=None) * 100

    # Momentum and oscillator family (12-24)
    out["12_roc_12"] = c.pct_change(12, fill_method=None) * 100
    out["13_momentum_10"] = c.diff(10)
    delta = c.diff(); gain = delta.clip(lower=0); loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(alpha=1/14, adjust=False, min_periods=14).mean()
    avg_loss = loss.ewm(alpha=1/14, adjust=False, min_periods=14).mean()
    out["14_rsi_14"] = 100 - (100 / (1 + safe(avg_gain / avg_loss)))
    low14, high14 = l.rolling(14).min(), h.rolling(14).max()
    stoch = safe(100 * (c - low14) / (high14 - low14))
    out["15_stochastic_k"] = stoch
    out["16_stochastic_d"] = stoch.rolling(3).mean()
    out["17_williams_r"] = safe(-100 * (high14 - c) / (high14 - low14))
    typical = (h + l + c) / 3
    tp_mean = typical.rolling(20).mean()
    mean_dev = typical.rolling(20).apply(lambda a: np.mean(np.abs(a - a.mean())), raw=True)
    out["18_cci_20"] = safe((typical - tp_mean) / (0.015 * mean_dev))
    prev = c.shift(1)
    buy_pressure = c - pd.concat([l, prev], axis=1).min(axis=1)
    true_range = pd.concat([h - l, (h - prev).abs(), (l - prev).abs()], axis=1).max(axis=1)
    out["19_ultimate_oscillator"] = safe(100 * (
        4 * buy_pressure.rolling(7).sum() / true_range.rolling(7).sum()
        + 2 * buy_pressure.rolling(14).sum() / true_range.rolling(14).sum()
        + buy_pressure.rolling(28).sum() / true_range.rolling(28).sum()
    ) / 7)
    midpoint = (h + l) / 2
    out["20_awesome_oscillator"] = midpoint.rolling(5).mean() - midpoint.rolling(34).mean()
    r1, r2, r3, r4 = (c.pct_change(n, fill_method=None) * 100 for n in (10, 15, 20, 30))
    out["21_kst"] = r1.rolling(10).sum() + 2*r2.rolling(10).sum() + 3*r3.rolling(10).sum() + 4*r4.rolling(15).sum()
    pc = c.diff(); apc = pc.abs()
    out["22_tsi"] = safe(100 * ema(ema(pc, 25), 13) / ema(ema(apc, 25), 13))
    streak = pd.Series(0.0, index=c.index)
    for i in range(1, len(c)):
        direction = np.sign(c.iloc[i] - c.iloc[i - 1])
        prior = streak.iloc[i - 1]
        streak.iloc[i] = 0 if direction == 0 else direction * (abs(prior) + 1 if np.sign(prior) == direction else 1)
    def rsi_series(x, n):
        d = x.diff(); g = d.clip(lower=0).ewm(alpha=1/n, adjust=False, min_periods=n).mean()
        q = (-d.clip(upper=0)).ewm(alpha=1/n, adjust=False, min_periods=n).mean()
        return 100 - 100 / (1 + safe(g / q))
    pct_rank = c.pct_change(fill_method=None).rolling(100).apply(lambda a: 100 * (a[-1] > a[:-1]).mean(), raw=True)
    out["23_connors_rsi"] = (rsi_series(c, 3) + rsi_series(streak, 2) + pct_rank) / 3
    sum_gain, sum_loss = gain.rolling(14).sum(), loss.rolling(14).sum()
    out["24_cmo_14"] = safe(100 * (sum_gain - sum_loss) / (sum_gain + sum_loss))

    # Volatility and channel family (25-36)
    mid = c.rolling(20).mean(); std = c.rolling(20).std(ddof=0)
    upper, lower = mid + 2*std, mid - 2*std
    out["25_bollinger_upper"] = upper
    out["26_bollinger_lower"] = lower
    out["27_bollinger_bandwidth"] = safe(100 * (upper - lower) / mid)
    atr = true_range.ewm(alpha=1/14, adjust=False, min_periods=14).mean()
    out["28_atr_14"] = atr
    out["29_natr_14"] = safe(100 * atr / c)
    out["30_true_range"] = true_range
    kel_mid = ema(c, 20)
    out["31_keltner_upper"] = kel_mid + 2 * atr
    out["32_keltner_lower"] = kel_mid - 2 * atr
    out["33_donchian_upper"] = h.rolling(20).max()
    out["34_donchian_lower"] = l.rolling(20).min()
    out["35_stddev_20"] = std
    out["36_historical_volatility"] = np.log(c / c.shift(1)).rolling(20).std(ddof=0) * np.sqrt(252) * 100

    # Directional, stop and cloud family (37-46)
    up_move, down_move = h.diff(), -l.diff()
    plus_dm = up_move.where((up_move > down_move) & (up_move > 0), 0.0)
    minus_dm = down_move.where((down_move > up_move) & (down_move > 0), 0.0)
    plus_di = safe(100 * plus_dm.ewm(alpha=1/14, adjust=False, min_periods=14).mean() / atr)
    minus_di = safe(100 * minus_dm.ewm(alpha=1/14, adjust=False, min_periods=14).mean() / atr)
    out["37_adx_14"] = safe(100 * (plus_di - minus_di).abs() / (plus_di + minus_di)).ewm(alpha=1/14, adjust=False, min_periods=14).mean()
    out["38_plus_di"] = plus_di
    out["39_minus_di"] = minus_di
    out["40_aroon_up"] = h.rolling(25).apply(lambda a: 100 * (np.argmax(a) + 1) / len(a), raw=True)
    out["41_aroon_down"] = l.rolling(25).apply(lambda a: 100 * (np.argmin(a) + 1) / len(a), raw=True)
    out["42_vortex_plus"] = safe((h - l.shift(1)).abs().rolling(14).sum() / true_range.rolling(14).sum())
    out["43_vortex_minus"] = safe((l - h.shift(1)).abs().rolling(14).sum() / true_range.rolling(14).sum())
    psar = pd.Series(np.nan, index=c.index)
    if len(c):
        bull, af, extreme = True, 0.02, h.iloc[0]
        psar.iloc[0] = l.iloc[0]
        for i in range(1, len(c)):
            candidate = psar.iloc[i-1] + af * (extreme - psar.iloc[i-1])
            if bull:
                candidate = min(candidate, l.iloc[i-1], l.iloc[max(i-2, 0)])
                if l.iloc[i] < candidate: bull, candidate, af, extreme = False, extreme, 0.02, l.iloc[i]
                elif h.iloc[i] > extreme: extreme, af = h.iloc[i], min(af + 0.02, 0.2)
            else:
                candidate = max(candidate, h.iloc[i-1], h.iloc[max(i-2, 0)])
                if h.iloc[i] > candidate: bull, candidate, af, extreme = True, extreme, 0.02, h.iloc[i]
                elif l.iloc[i] < extreme: extreme, af = l.iloc[i], min(af + 0.02, 0.2)
            psar.iloc[i] = candidate
    out["44_parabolic_sar"] = psar
    out["45_ichimoku_conversion"] = (h.rolling(9).max() + l.rolling(9).min()) / 2
    out["46_ichimoku_base"] = (h.rolling(26).max() + l.rolling(26).min()) / 2

    # Volume and money-flow family (47-50)
    out["47_obv"] = (np.sign(c.diff()).fillna(0) * v).cumsum()
    raw_money = typical * v; positive = raw_money.where(typical.diff() > 0, 0); negative = raw_money.where(typical.diff() < 0, 0)
    out["48_mfi_14"] = 100 - 100 / (1 + safe(positive.rolling(14).sum() / negative.rolling(14).sum()))
    money_flow_multiplier = safe(((c - l) - (h - c)) / (h - l))
    money_flow_volume = money_flow_multiplier * v
    out["49_cmf_20"] = safe(money_flow_volume.rolling(20).sum() / v.rolling(20).sum())
    out["50_accumulation_distribution"] = money_flow_volume.fillna(0).cumsum()
    assert out.shape[1] == 50
    return out

Run all 50 on reproducible demo OHLCV¶

rng = np.random.default_rng(7)
n = 320
close = pd.Series(22000 + rng.normal(0, 70, n).cumsum())
demo = pd.DataFrame({
    "open": close.shift(1).fillna(close.iloc[0]),
    "high": close + rng.uniform(10, 90, n),
    "low": close - rng.uniform(10, 90, n),
    "close": close,
    "volume": rng.integers(100_000, 900_000, n),
}, index=pd.date_range("2025-01-01", periods=n, freq="B"))

indicators = compute_top_50(demo)
print("indicator count:", indicators.shape[1])
display(indicators.tail(5).T)
assert indicators.shape[1] == 50

Avoid the three common research errors¶

  • Warm-up leakage: keep early NaN values; do not backfill an indicator with future information.
  • Same-bar fills: a signal using a candle close can normally act only on the next tradable event in a bar-based backtest.
  • Unadjusted data: splits, bonuses, symbol changes and futures rolls can create fake signals. Use broker/exchange metadata and document adjustments.

For production, compare a sample against a second implementation. Small differences can come from Wilder versus EMA smoothing, population versus sample deviation, and candle/session boundaries.

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