Seasonal Crude Oil Pattern

Crude oil futures follow some of the most reliable seasonal patterns in the commodities universe. Traders who understand the cyclical forces behind /CL price action gain a structural edge that purely technical or fundamental approaches cannot replicate on their own. From the summer driving season to winter heating demand and the refinery maintenance windows in between, these recurring patterns create tradeable opportunities year after year.

This guide breaks down the seasonal crude oil trading pattern in full detail, with actionable ThinkOrSwim setups, ThinkScript code blocks, and a framework for combining seasonality with your existing thinkorswim indicators and tools.

Understanding the Three Core Seasonal Cycles in Crude Oil

Crude oil demand does not stay flat throughout the year. Three primary cycles drive predictable shifts in supply, demand, and price behavior for /CL futures. Each cycle creates a distinct trading environment that rewards different strategies.

The first cycle is the summer driving season, which typically runs from late April through early September. Gasoline demand surges as Americans hit the road for vacations and travel. Refineries ramp up production, pulling crude oil inventories lower and placing upward pressure on prices.

The second cycle is the winter heating demand period, spanning from October through February. Heating oil and distillate consumption rises across the northern hemisphere. This cycle tends to support prices heading into winter, though the magnitude depends heavily on weather severity.

The third cycle involves refinery maintenance turnarounds, which occur primarily in March/April and September/October. During these windows, refineries go offline for scheduled maintenance, temporarily reducing crude demand and often creating short-term price weakness.

Monthly Seasonal Tendencies for /CL Futures

Breaking down crude oil performance by month reveals where the seasonal tendencies are strongest. The table below summarizes the average directional bias, typical price range, and historical win rate for long positions by month over the past 22 years of /CL futures data.

The pattern is clear: the February-to-June window represents the strongest seasonal bullish period for crude oil. September and October are historically the weakest months. Traders using thinkorswim indicators can layer these seasonal tendencies into their existing technical setups for improved timing.

The Summer Driving Season: Crude Oil's Strongest Seasonal Catalyst

The summer driving season is the single biggest demand driver for crude oil in the United States. The Energy Information Administration (EIA) reports that gasoline consumption typically rises 5% to 8% above the annual average between Memorial Day and Labor Day.

This demand surge does not begin on Memorial Day, though. The price impact starts weeks earlier as refineries increase crude oil throughput to build gasoline inventories. The smart money begins positioning in late March and April, well before the retail narrative of "summer driving" takes hold.

For /CL futures traders, the optimal seasonal entry window historically falls between mid-April and early May. Holding through mid-June captures the bulk of the seasonal rally without exposure to the mid-summer plateau where prices often stall.

Winter Heating Demand and Its Impact on /CL Pricing

While natural gas gets most of the attention during winter, crude oil prices also respond to cold weather demand. Heating oil (distillate fuel) accounts for a meaningful portion of the petroleum demand barrel, particularly in the northeastern United States.

The seasonal pattern shows crude prices firming from late October through early January as the market prices in winter heating demand. However, this pattern is less reliable than the summer driving season because it depends heavily on weather. A mild winter can completely negate the seasonal bullish tendency.

Traders who combine winter seasonal analysis with weather forecasts and the Volatility Box for real-time volatility measurement gain a significant edge during this period. When the TTM Squeeze fires on the daily chart during a cold weather snap in November or December, it often signals the start of a high-probability seasonal move.

Refinery Maintenance Turnarounds: The Hidden Seasonal Cycle

Refinery turnarounds are the least discussed seasonal factor in crude oil, but they create some of the most predictable short-term price patterns. U.S. refineries typically schedule major maintenance during two windows: March/April (spring turnarounds) and September/October (fall turnarounds).

During turnarounds, refineries reduce crude oil throughput. This temporarily decreases crude demand and can lead to inventory builds. The market typically responds with short-term price weakness, especially in the fall turnaround window when it coincides with the post-summer demand decline.

The spring turnaround creates a different dynamic. While refinery demand temporarily dips, the market looks ahead to the summer driving season. This forward-looking behavior means the spring turnaround dip is often shallow and quickly reversed, making it a potential buying opportunity.

ThinkScript Seasonal Overlay for /CL Futures

One of the best ways to incorporate seasonal analysis into your ThinkOrSwim workflow is building a custom seasonal overlay study. The following ThinkScript creates a visual reference for crude oil's historical monthly bias directly on your chart. This is one of the more practical thinkorswim scripts for day trading crude oil futures.

# Crude Oil Seasonal Bias Indicator
# Displays monthly seasonal tendency as background color
# For use on /CL futures daily charts

declare upper;

def month = GetMonth();

# Seasonal bias scoring (positive = bullish, negative = bearish)
def seasonalScore =
  if month == 1 then -1
  else if month == 2 then 0.5
  else if month == 3 then 1
  else if month == 4 then 1.5
  else if month == 5 then 2
  else if month == 6 then 1.5
  else if month == 7 then 0
  else if month == 8 then -0.5
  else if month == 9 then -1.5
  else if month == 10 then -1
  else if month == 11 then 0.5
  else 0;

# Background color based on seasonal bias
AssignBackgroundColor(
  if seasonalScore >= 1.5 then Color.DARK_GREEN
  else if seasonalScore >= 0.5 then Color.DARK_GRAY
  else if seasonalScore <= -1 then Color.DARK_RED
  else Color.BLACK
);

AddLabel(yes,
  "Seasonal Bias: " +
  (if seasonalScore >= 1.5 then "STRONG BULL"
   else if seasonalScore >= 0.5 then "MILD BULL"
   else if seasonalScore <= -1 then "BEARISH"
   else "NEUTRAL"),
  if seasonalScore >= 1.5 then Color.GREEN
  else if seasonalScore >= 0.5 then Color.YELLOW
  else if seasonalScore <= -1 then Color.RED
  else Color.GRAY
);

This study provides a quick visual reference for the seasonal backdrop. Green shading highlights the bullish April-June window, while red shading marks the bearish September-October period. The label at the top of the chart gives you an instant read on the current seasonal bias.

Building a Seasonal Scanner in ThinkOrSwim

Beyond chart overlays, you can use thinkorswim scanners to find energy-sector stocks and ETFs that align with crude oil seasonal patterns. The following ThinkScript scanner identifies energy names showing bullish momentum during the seasonally favorable window.

# Seasonal Crude Oil Momentum Scanner
# Scans for energy stocks with bullish alignment
# during favorable seasonal months

def month = GetMonth();
def inBullSeason = month >= 3 and month <= 6;
def inBearSeason = month >= 9 and month <= 10;

def rsi14 = RSI(length = 14);
def sma50 = Average(close, 50);
def sma200 = Average(close, 200);
def vol20 = Average(volume, 20);

# Bull season scan: RSI rising + above 50/200 SMA
def bullSignal = inBullSeason
  and close > sma50
  and sma50 > sma200
  and rsi14 > 50
  and rsi14 < 70
  and volume > vol20 * 1.1;

# Bear season scan: RSI falling + below 50 SMA
def bearSignal = inBearSeason
  and close < sma50
  and rsi14 < 50
  and rsi14 > 30;

plot scan = if inBullSeason then bullSignal else bearSignal;

This scanner, along with other thinkorswim scripts for day trading, can be customized to your preferences. Run this scanner against a watchlist containing energy stocks (XLE components, XOP components) and crude oil ETFs (USO, UCO, SCO). During the March-June seasonal window, the scanner will flag names showing bullish alignment with the seasonal pattern. This is one of the more effective thinkorswim scanners for energy sector trading.

Combining Seasonality with the TTM Squeeze on ThinkOrSwim

Seasonal analysis works best when combined with technical confirmation. The TTM Squeeze on ThinkOrSwim (often searched as "ttm squeeze thinkorswim") is one of the most effective tools for timing seasonal entries because it identifies periods of price compression that precede directional moves.

Here is how to combine seasonality with the TTM Squeeze for /CL futures:

Step 1: Identify the current seasonal window using the monthly table above or the ThinkScript seasonal overlay.

Step 2: During bullish seasonal windows (March-June), wait for the TTM Squeeze to fire on the daily chart. The squeeze dots should transition from red (squeeze on) to green (squeeze firing).

Step 3: Confirm that the TTM Squeeze momentum histogram is positive and rising. This indicates the squeeze is releasing in the direction that aligns with the seasonal bias.

Step 4: Enter long positions on /CL when the squeeze fires with positive momentum during the bullish seasonal window. Place stops below the recent squeeze low.

# Seasonal Squeeze Confirmation for /CL
# Combines TTM Squeeze with seasonal bias filter
# For daily chart application

declare lower;

input bbLength = 20;
input bbMult = 2.0;
input kcLength = 20;
input kcMult = 1.5;

def month = GetMonth();
def bullSeason = month >= 3 and month <= 6;
def bearSeason = month >= 9 and month <= 10;

# Bollinger Band calculations
def bbUpper = Average(close, bbLength) + bbMult * StDev(close, bbLength);
def bbLower = Average(close, bbLength) - bbMult * StDev(close, bbLength);

# Keltner Channel calculations
def trueRange = TrueRange(high, close, low);
def atr = Average(trueRange, kcLength);
def kcUpper = Average(close, kcLength) + kcMult * atr;
def kcLower = Average(close, kcLength) - kcMult * atr;

# Squeeze detection
def squeezeOn = bbLower > kcLower and bbUpper < kcUpper;
def squeezeFiring = !squeezeOn and squeezeOn[1];

# Momentum
def mom = close - Average(close, bbLength);

# Seasonal squeeze signal
plot seasonalBullSignal = bullSeason and squeezeFiring and mom > 0;
plot seasonalBearSignal = bearSeason and squeezeFiring and mom < 0;

seasonalBullSignal.SetPaintingStrategy(PaintingStrategy.BOOLEAN_ARROW_UP);
seasonalBullSignal.SetDefaultColor(Color.GREEN);
seasonalBearSignal.SetPaintingStrategy(PaintingStrategy.BOOLEAN_ARROW_DOWN);
seasonalBearSignal.SetDefaultColor(Color.RED);

This study plots arrows on the chart when a TTM Squeeze fires in alignment with the seasonal direction. Green up arrows appear during bullish seasonal months when the squeeze releases with positive momentum. Red down arrows appear during bearish seasonal months with negative momentum.

Volatility Patterns Across Crude Oil Seasonal Cycles

Volatility in /CL futures does not remain constant throughout the year. Understanding how volatility shifts across seasonal cycles helps you size positions correctly and set appropriate stop distances.

Notice that volatility tends to peak during the September-October period when seasonal bearish pressure coincides with geopolitical uncertainty and OPEC decision cycles. The Volatility Box indicator measures real-time volatility conditions and automatically adjusts its levels to account for these seasonal shifts, giving you dynamic support and resistance targets that adapt throughout the year.

OPEC Meetings and Their Interaction with Seasonal Patterns

OPEC and OPEC+ ministerial meetings occur on a semi-annual schedule, typically in June and November/December. These meetings can reinforce or disrupt seasonal patterns depending on the production decisions announced.

The June OPEC meeting is particularly significant because it falls at the tail end of the bullish seasonal window. A production increase announcement can cap or reverse the seasonal rally. Conversely, a production cut or maintenance of existing cuts during the June meeting can extend the bullish seasonal trend deeper into summer.

The November/December meeting interacts with the winter demand cycle. Production cuts heading into winter amplify the seasonal bullish tendency, while production increases during this window can negate it entirely.

For traders using thinkorswim indicators, the key is to reduce position sizes and widen stops heading into OPEC meetings. The binary outcome risk does not align well with seasonal trend strategies that depend on gradual, predictable demand shifts.

EIA Inventory Reports and Seasonal Trading

The weekly EIA Petroleum Status Report (released every Wednesday at 10:30 AM ET) is the most important recurring data point for crude oil traders. Inventory changes have a seasonal rhythm that directly maps to the cycles described above.

During the driving season buildup (April-May), traders should watch for draws in crude oil inventories and builds in gasoline inventories. This combination signals that refineries are actively converting crude into gasoline, which is the seasonal demand pattern you want to see supporting a long position.

During the fall turnaround (September-October), expect crude inventory builds as refinery throughput declines. These builds are normal and expected, but the market can still react negatively if the builds exceed consensus estimates.

Combine EIA report analysis with your Volatility Box for futures levels. If crude inventories draw more than expected during the bullish seasonal window and price pulls back to a Volatility Box support level, that confluence creates a high-probability long setup.

Risk Management for Seasonal /CL Trades

Seasonal trading requires a different risk management framework than short-term technical trading. The holding periods are longer (weeks to months), and the trade thesis depends on macro demand cycles rather than chart patterns alone.

Here are the core risk management rules for seasonal crude oil trades:

Position Sizing: Limit seasonal /CL positions to 2-3% of account equity per trade. Each $1 move in /CL equals $1,000 per contract, so a $5 adverse move on two contracts costs $10,000. Size accordingly.

Stop Placement: Use ATR-based stops set at 2.5x to 3x the 14-period daily ATR. During the March-June bullish window, this typically translates to stops $4 to $6 below entry on long positions.

Profit Targets: Seasonal trades should target the historical average seasonal move. For the spring-to-summer rally, this averages $8 to $12 per barrel from the March low to the June high.

Time Stops: If a seasonal trade has not begun moving in the expected direction within 15 to 20 trading days, consider exiting. The seasonal pattern may be disrupted by geopolitical events, OPEC decisions, or shifting macro conditions.

Backtesting Seasonal Strategies with ThinkOrSwim

ThinkOrSwim's Strategy Tester allows you to backtest seasonal crude oil approaches. Below is a ThinkScript strategy that enters long during the bullish seasonal window and short during the bearish window, providing a starting framework for your own backtesting.

# Seasonal Crude Oil Backtest Strategy
# Long bias: March through June
# Short bias: September through October
# Flat: All other months

def month = GetMonth();
def dayOfMonth = GetDayOfMonth(GetYYYYMMDD());

# Entry conditions
def longEntry = month == 3 and dayOfMonth >= 10 and dayOfMonth <= 15;
def longExit = month == 6 and dayOfMonth >= 15 and dayOfMonth <= 20;
def shortEntry = month == 9 and dayOfMonth >= 10 and dayOfMonth <= 15;
def shortExit = month == 10 and dayOfMonth >= 20 and dayOfMonth <= 25;

AddOrder(OrderType.BUY_TO_OPEN,
  condition = longEntry,
  price = open[-1],
  tradeSize = 1,
  name = "Seasonal Long");

AddOrder(OrderType.SELL_TO_CLOSE,
  condition = longExit,
  price = open[-1],
  tradeSize = 1,
  name = "Seasonal Long Exit");

AddOrder(OrderType.SELL_TO_OPEN,
  condition = shortEntry,
  price = open[-1],
  tradeSize = 1,
  name = "Seasonal Short");

AddOrder(OrderType.BUY_TO_CLOSE,
  condition = shortExit,
  price = open[-1],
  tradeSize = 1,
  name = "Seasonal Short Exit");

Load this strategy onto a /CL daily chart, then open the Strategy Report to review historical performance. Pay attention to the win rate, average winning trade size, and maximum drawdown. This baseline strategy can be refined by adding filters from the Volatility Box or the ttm squeeze thinkorswim confirmation logic shown earlier. Combining these thinkorswim scripts for day trading with seasonal filters creates a powerful backtesting framework.

Using the Volatility Box for Seasonal /CL Entries and Exits

The Volatility Box generates dynamic support and resistance levels based on real-time volatility calculations. When combined with seasonal analysis, it provides precise entry and exit points for /CL trades.

During the bullish seasonal window (March-June), use the Volatility Box lower levels as potential entry points for long positions. When /CL pulls back to a Volatility Box support level during a seasonally bullish month, the combination of technical support and seasonal tailwind creates a high-confidence trade setup.

During the bearish seasonal window (September-October), use the Volatility Box upper levels as resistance targets for short entries or profit-taking on remaining long positions.

The Volatility Box for futures is specifically calibrated for the tick sizes and volatility profiles of futures contracts including /CL. The levels automatically adjust as crude oil volatility shifts throughout the seasonal cycles described in this guide.

Common Mistakes in Seasonal Crude Oil Trading

Even experienced traders make avoidable errors when trading crude oil seasonality. Understanding these pitfalls will help you extract maximum value from seasonal analysis.

Mistake #1: Trading seasonality in isolation. Seasonal patterns describe historical probabilities, not certainties. A seasonal long bias means nothing if crude oil is in a strong downtrend driven by recession fears or a supply glut. Always confirm seasonal direction with technical tools like the TTM Squeeze and the Volatility Box.

Mistake #2: Entering too early or too late. The spring rally typically begins in mid-March, not January. Entering a seasonal long in January because "crude usually rallies in the first half" ignores the January-February weakness that precedes the rally. Timing matters as much as direction.

Mistake #3: Ignoring geopolitical overrides. Wars, sanctions, pipeline disruptions, and OPEC production decisions can override seasonal patterns. When crude oil is driven by headline risk, seasonal analysis takes a back seat. Reduce size or step aside during periods of elevated geopolitical uncertainty.

Mistake #4: Using incorrect contract months. /CL futures expire monthly. Make sure you are trading the correct front-month contract and account for roll dates. The contract roll can create temporary price distortions that look like seasonal moves but are simply the price difference between the expiring and new front-month contracts.

Mistake #5: Failing to adjust for the current year's fundamentals. Each year is different. A year with record U.S. shale production may dampen the summer driving season rally. A year with tight global inventories may amplify it. Review current supply/demand fundamentals before assuming the average seasonal pattern will play out.

Putting It All Together: A Seasonal /CL Trading Plan

Here is a complete framework for trading crude oil seasonality with ThinkOrSwim:

Phase 1 (January-February): Research and preparation. Review current supply/demand fundamentals, OPEC policy, and U.S. production data. Load the seasonal overlay study onto your /CL daily chart. Identify potential entry zones using the Volatility Box for futures.

Phase 2 (March-April): Begin positioning for the spring rally. Watch for TTM Squeeze setups on the daily chart. Enter long positions when the squeeze fires with positive momentum during this window. Initial stops at 2.5x ATR below entry.

Phase 3 (May-June): Manage existing positions. Trail stops using the 20-day moving average or Volatility Box mid-level. Scale out of 50% of the position by mid-June. Be prepared to exit fully before major OPEC meetings.

Phase 4 (July-August): Reduce exposure. The seasonal tailwind fades during mid-summer. Exit remaining long positions and move to the sidelines or trade range-bound strategies.

Phase 5 (September-October): Look for short opportunities. The fall turnaround creates seasonal headwinds. Use the TTM Squeeze and Volatility Box resistance levels to time short entries. Keep position sizes smaller due to elevated volatility.

Phase 6 (November-December): Evaluate winter demand setup. If cold weather forecasts and tight inventories support it, begin building long positions for the winter heating demand cycle. If fundamentals do not support the seasonal long thesis, stay flat and prepare for the next spring cycle.