Simple moving average crossover strategies are among the oldest systematic trading rules in existence — and for good reason. They are transparent, easy to implement, and surprisingly competitive against more complex models over long investment horizons. For a high-volatility, trend-driven stock like Palantir Technologies (PLTR), a well-tuned SMA system can serve as a practical first filter for managing long-term exposure without requiring a machine learning pipeline or exotic data sources.
In this article, you will build a complete SMA backtesting engine from scratch using Python, pandas, and yfinance. You will implement a dual-SMA crossover strategy, vectorize the signal generation and return calculation logic, run a parameter grid search over short and long window combinations, and evaluate each configuration using Sharpe ratio, maximum drawdown, and total return. By the end, you will have a reusable framework you can apply to any ticker with minimal modification.
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This article covers:
- Section 1 — The SMA Crossover Strategy:** What moving average crossovers are, how they generate signals, and why they suit trend-following in volatile equities
- Section 2 — Python Implementation:** Full setup, data download, signal generation, vectorized backtesting engine, parameter grid search, and equity curve visualization
- Section 3 — Results and Analysis:** What the grid search reveals about optimal window lengths, Sharpe ratios, and drawdown profiles for PLTR
- Section 4 — Use Cases:** Practical contexts where this framework adds real value
- Section 5 — Limitations and Edge Cases:** Honest discussion of overfitting, look-ahead bias, and transaction cost blindspots
1. The SMA Crossover Strategy
A simple moving average smooths a price series by computing the arithmetic mean of the last N closing prices. As new prices arrive, the window slides forward and the average updates. A single SMA tells you roughly where price has been on average — but the real signal comes from comparing two SMAs with different lookback windows.
The classic crossover rule works like this: you define a short-window SMA (fast line) and a long-window SMA (slow line). When the fast line crosses above the slow line, recent price momentum has turned upward relative to the longer trend — a buy signal. When the fast line crosses below the slow line, momentum has deteriorated — a sell or exit signal. This logic captures trend changes without requiring any prediction of future prices, only a reaction to confirmed price behavior.
For PLTR specifically, this matters because the stock exhibits extended directional moves — both upward and downward — driven by earnings surprises, government contract announcements, and broader growth-sector rotations. A short-term SMA of 20 days paired with a long-term SMA of 100 days, for example, will keep you invested through multi-month rallies while cutting exposure during sustained drawdowns. The challenge is that no single pair of window lengths is universally optimal, which is exactly why systematic parameter tuning is worth building.
The mathematical backbone is straightforward. For a price series P and window length n, the SMA at time t is defined as the mean of the n most recent closing prices. A long position is held when SMA_short(t) > SMA_long(t), and cash is held otherwise. Daily strategy returns equal the daily log return of PLTR when the signal is active and zero otherwise.
2. Python Implementation
2.1 Setup and Parameters
The configurable parameters are the ticker symbol, date range, and the grid of SMA window lengths to test. The short window grid covers faster-moving averages that react quickly to price changes; the long window grid covers slower-moving averages that define the dominant trend. Wider grids give better coverage but increase computation time quadratically.
# ── Dependencies ──────────────────────────────────────────────────────────────
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from itertools import product
# ── Configuration ─────────────────────────────────────────────────────────────
TICKER = "PLTR"
START_DATE = "2020-10-01" # PLTR IPO was September 30, 2020
END_DATE = "2025-06-30"
RISK_FREE = 0.045 # annualised risk-free rate (approximate 2024 T-bill)
# Parameter grid
SHORT_WINDOWS = [10, 20, 30, 50] # fast SMA candidates (days)
LONG_WINDOWS = [60, 100, 150, 200] # slow SMA candidates (days)
INITIAL_CAPITAL = 10_000.0 # starting portfolio value in USD
2.2 Data Download and Signal Generation
This block downloads adjusted closing prices for PLTR, computes both SMAs for every valid parameter combination, and generates a binary position signal (1 = long, 0 = cash). The signal is lagged by one day to eliminate look-ahead bias — you can only act on a crossover that you observed at yesterday's close.
# ── Download price data ───────────────────────────────────────────────────────
raw = yf.download(TICKER, start=START_DATE, end=END_DATE, auto_adjust=True, progress=False)
prices = raw["Close"].squeeze().dropna()
log_returns = np.log(prices / prices.shift(1))
print(f"Loaded {len(prices)} trading days for {TICKER}")
print(f"Date range: {prices.index[0].date()} → {prices.index[-1].date()}")
print(f"Price range: ${prices.min():.2f} – ${prices.max():.2f}")
# ── Signal generation function ────────────────────────────────────────────────
def generate_signals(prices: pd.Series, short_w: int, long_w: int) -> pd.Series:
"""
Returns a daily position Series (1 = long, 0 = flat).
Signal is shifted forward by 1 day to prevent look-ahead bias.
"""
sma_short = prices.rolling(window=short_w).mean()
sma_long = prices.rolling(window=long_w).mean()
raw_signal = (sma_short > sma_long).astype(int)
return raw_signal.shift(1).fillna(0) # act on yesterday's signal
2.3 Vectorized Backtesting Engine and Grid Search
The backtesting engine multiplies the daily position signal by the daily log return to produce strategy returns, then computes cumulative equity, Sharpe ratio, and maximum drawdown for each parameter pair. The entire grid is evaluated in a single loop and results are stored in a tidy DataFrame for easy ranking.
# ── Performance metrics ───────────────────────────────────────────────────────
def compute_metrics(strategy_log_returns: pd.Series, rf: float = RISK_FREE) -> dict:
ann_return = strategy_log_returns.sum() * (252 / len(strategy_log_returns))
ann_vol = strategy_log_returns.std() * np.sqrt(252)
sharpe = (ann_return - rf) / ann_vol if ann_vol > 0 else np.nan
cum_returns = np.exp(strategy_log_returns.cumsum())
rolling_max = cum_returns.cummax()
drawdown = (cum_returns - rolling_max) / rolling_max
max_dd = drawdown.min()
total_return = np.exp(strategy_log_returns.sum()) - 1
n_trades = int((strategy_log_returns != 0).diff().fillna(0).abs().sum() / 2)
return {
"ann_return": ann_return,
"ann_vol": ann_vol,
"sharpe": sharpe,
"max_dd": max_dd,
"total_return": total_return,
"n_trades": n_trades,
}
# ── Grid search ───────────────────────────────────────────────────────────────
results = []
for short_w, long_w in product(SHORT_WINDOWS, LONG_WINDOWS):
if short_w >= long_w:
continue # enforce short < long constraint
signal = generate_signals(prices, short_w, long_w)
strat_returns = signal * log_returns
metrics = compute_metrics(strat_returns)
metrics.update({"short_w": short_w, "long_w": long_w})
results.append(metrics)
results_df = (
pd.DataFrame(results)
.sort_values("sharpe", ascending=False)
.reset_index(drop=True)
)
# Display top 10 configurations
display_cols = ["short_w", "long_w", "sharpe", "total_return", "max_dd", "ann_vol", "n_trades"]
print(results_df[display_cols].head(10).to_string(index=False))
2.4 Visualization
The chart below plots the equity curves for the top five parameter combinations alongside a buy-and-hold baseline. This gives you an immediate visual sense of which configurations preserved capital during PLTR's major drawdowns while still capturing the large upward moves.
# ── Equity curve plot ─────────────────────────────────────────────────────────
plt.style.use("dark_background")
fig, axes = plt.subplots(2, 1, figsize=(13, 9), gridspec_kw={"height_ratios": [3, 1]})
ax_equity, ax_dd = axes
# Buy-and-hold baseline
bh_equity = np.exp(log_returns.cumsum()) * INITIAL_CAPITAL
ax_equity.plot(bh_equity.index, bh_equity.values,
color="#888888", linewidth=1.2, linestyle="--", label="Buy & Hold")
# Top 5 SMA configurations
colors = ["#00BFFF", "#FF6B6B", "#7FFF00", "#FFD700", "#DA70D6"]
top5 = results_df.head(5)
for idx, (_, row) in enumerate(top5.iterrows()):
short_w, long_w = int(row["short_w"]), int(row["long_w"])
signal = generate_signals(prices, short_w, long_w)
strat_ret = signal * log_returns
equity = np.exp(strat_ret.cumsum()) * INITIAL_CAPITAL
# Drawdown
rolling_max = equity.cummax()
dd_series = (equity - rolling_max) / rolling_max
label = f"SMA({short_w},{long_w}) | Sharpe={row['sharpe']:.2f}"
ax_equity.plot(equity.index, equity.values,
color=colors[idx], linewidth=1.5, label=label)
ax_dd.fill_between(dd_series.index, dd_series.values, 0,
color=colors[idx], alpha=0.35)
# Format equity axis
ax_equity.set_title(f"{TICKER} — SMA Crossover Strategy: Top 5 Configurations",
fontsize=14, fontweight="bold", pad=12)
ax_equity.set_ylabel("Portfolio Value (USD)", fontsize=11)
ax_equity.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"${x:,.0f}"))
ax_equity.legend(fontsize=9, loc="upper left", framealpha=0.3)
ax_equity.grid(alpha=0.15)
# Format drawdown axis
ax_dd.set_title("Drawdown", fontsize=11, pad=6)
ax_dd.set_ylabel("Drawdown (%)", fontsize=10)
ax_dd.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.0f}%"))
ax_dd.set_xlabel("Date", fontsize=10)
ax_dd.grid(alpha=0.15)
plt.tight_layout(h_pad=2.5)
plt.savefig("pltr_sma_backtest.png", dpi=150, bbox_inches="tight")
plt.show()
Figure 1. Equity curves for the top five SMA parameter combinations versus buy-and-hold for PLTR from October 2020 to June 2025; the lower panel shows the corresponding drawdown profiles, highlighting how aggressive window pairs absorb sharper peak-to-trough declines.
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3. Results and Analysis
Running the grid search across the 12 valid short/long window combinations typically surfaces a meaningful spread in risk-adjusted performance. Across PLTR's full trading history from its IPO through mid-2025 — a period that includes the 2021 retail-driven spike, the severe 2022 drawdown, and the 2023–2025 recovery — the faster parameter pairs like SMA(10, 60) tend to produce higher trade counts and capture more of the upside, while slower pairs like SMA(30, 200) show shallower maximum drawdowns with lower overall returns.
The Sharpe ratios in this framework typically range from approximately 0.4 to 1.1 depending on the window pair, compared to a buy-and-hold Sharpe that can swing dramatically given PLTR's realized volatility of 60–80% annualized. The key insight is that the crossover strategy earns its risk-adjusted edge primarily by reducing exposure during sustained downtrends — most notably the 80% peak-to-trough decline PLTR experienced from late 2021 through late 2022 — rather than by timing entries with precision.
Maximum drawdown is where systematic SMA strategies show their clearest advantage over passive holding. While a buy-and-hold investor in PLTR would have experienced drawdowns exceeding 70%, even moderately calibrated SMA configurations tend to cut that figure by 20–35 percentage points. The trade-off is in total return: the strategy spends some periods in cash, missing rallies that follow false-negative crossovers. The optimal configuration for long-term investors typically sits in the middle of the grid — responsive enough to exit downtrends early but slow enough to avoid being whipsawed by short-lived volatility spikes.
4. Use Cases
Long-term position management for volatile growth stocks. PLTR, TSLA, NVDA, and similar high-beta names exhibit the extended trend behavior that SMA crossovers are designed to exploit. This framework gives long-term investors a systematic rule for scaling in and out rather than relying on discretionary judgment during volatile periods.
Baseline benchmark for more complex strategies. Before adding machine learning signals, sentiment features, or options overlays, it is good practice to establish what a simple rules-based system achieves. The SMA backtest serves as that baseline — if a more sophisticated model cannot consistently beat it on a risk-adjusted basis, the added complexity is not justified.
Multi-ticker screening tool. The
generate_signalsandcompute_metricsfunctions are fully generic. You can wrap the grid search in a second loop over a list of tickers to identify which names in a watchlist are currently in SMA-confirmed uptrends, producing a systematic daily filter with no manual chart reading.Educational foundation for systematic strategy development. The vectorized return calculation, Sharpe computation, and drawdown tracking implemented here appear in nearly every production-grade backtesting system. Understanding this foundation makes it significantly easier to work with frameworks like Backtrader, VectorBT, or Zipline later.
5. Limitations and Edge Cases
In-sample overfitting. The grid search tests every window combination on the same historical data used to evaluate performance. The best-performing parameters are likely to have benefited from noise specific to this time period. Without a proper walk-forward validation or out-of-sample holdout, reported Sharpe ratios are optimistic. Always reserve at least 20–30% of the data as a test set before drawing conclusions about live deployment.
Transaction costs and slippage are ignored. Each signal change triggers a full position switch. In practice, broker commissions, bid-ask spread, and market impact erode returns — particularly for higher-frequency parameter pairs that generate dozens of trades per year. For PLTR, a round-trip cost estimate of 0.10–0.20% per trade should be subtracted from gross returns before comparing configurations.
Binary position sizing. The strategy is either fully invested or fully in cash. Real portfolios use fractional position sizing, stop-losses, and volatility-scaled allocations. A 1% adverse move in PLTR while fully invested has the same impact whether the crossover signal was marginally positive or strongly positive, which is not how a well-constructed portfolio would behave.
Regime dependency. SMA crossover strategies perform well in trending markets and poorly in mean-reverting or choppy regimes. PLTR's post-2023 period has been more strongly trending than its 2021–2022 period, which is favorable for this approach. A regime classifier running in parallel — even a simple VIX threshold — can help suppress signals during periods where trend-following historically underperforms.
Survivorship and selection bias. Testing on PLTR specifically, a stock you already know has survived and recovered, introduces selection bias. The framework will produce more sobering results when applied to a random sample of growth stocks from 2020, many of which declined and never recovered.
Concluding Thoughts
SMA crossover backtesting is one of the most instructive exercises in quantitative finance precisely because it forces you to confront the core mechanics of systematic strategy design: signal generation, execution assumptions, performance attribution, and overfitting risk. The implementation built here — vectorized signal logic, a reusable metrics function, and a structured grid search — represents a foundation that scales directly into more advanced work.
The practical takeaway for PLTR specifically is that systematic trend-following has historically earned its keep through drawdown reduction rather than return enhancement. If your primary goal as a long-term investor is capital preservation during deep corrections while maintaining meaningful exposure to the upside, a calibrated SMA overlay is a defensible tool. The exact window lengths matter less than the discipline of following the rules consistently.
From here, the natural extensions are adding walk-forward validation to produce honest out-of-sample results, incorporating transaction cost models, and testing whether alternative signal filters — such as volume confirmation or volatility-adjusted entry thresholds — improve robustness. Each of those topics builds directly on the framework developed above.
Most algo trading content gives you theory.
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