Campaign Trading: Tactics and Strategies to Exploit the Markets

Author: John Sweeney | Categories: Trading Systems, Risk Management, Futures, Technical Analysis

Executive Summary

Campaign trading coordinates multiple tactics -- trend following, add-on day trades, add-on position trades, reversals, range trading, and range reversals -- into a unified strategy for exploiting a single tradable instrument over time. The book's central innovation is Maximum Adverse Excursion (MAE), a technique for setting stops based on the actual measured experience of how far winning versus losing trades move against you from the point of entry. Tested on 11 years of New York Light Crude data (1983-1994), Sweeney demonstrates that winners cluster at low MAE levels while losers spread across higher MAE bins, providing an empirical rather than arbitrary basis for stop placement. He extends MAE with Minimum Favorable Excursion (MinFE) for protective stop advancement and explores using options as flexible alternatives to hard stops.

Core Thesis & Arguments

Markets alternate between trending and ranging modes. A trader should exploit both modes using different tactics, all unified by MAE analysis. Stop placement should be based on measured experience -- not arbitrary dollar amounts or indicators. The campaign metaphor treats trading as continuous warfare: always having a position, always probing. Rather than seeking a single "holy grail" system, Sweeney argues that coordinating multiple simple tactics into a campaign produces superior results because each tactic captures profits the others miss. The key insight is that measuring from the point of entry -- not from arbitrary reference points -- reveals the true risk profile of a trading approach.

Chapter-by-Chapter Coverage

Chapter 1: Campaigning -- The military campaign metaphor applied to trading. Classic campaigns involve accumulation and distribution phases. The modern continuous campaign uses market indicators to specify whether the market is trending or ranging. Two core innovations are introduced: measuring everything from the point of entry, and measuring adverse and favorable excursion from that entry point.

Chapter 2: Trading the Trend -- Defining trend as persistence, meaning direction combined with duration. Using moving averages as filters. The two-MA system uses a 12-day short average and a 12-week (60-day) long average. Trading rule: go long when both averages are rising, go short when both are falling, stay out when they disagree.

Chapter 3: Handling the Bad News -- MAE concept introduced in full detail. Measuring the worst intraday move against a position from entry. The 7-step process: (1) define entry and exit rules, (2) tabulate daily MAE for each trade, (3) separate winners from losers, (4) tabulate MAE for each group, (5) sort into bins, (6) graph the summary table, (7) identify the stop level. Key finding: winners cluster at low MAE values while losers spread across higher MAE bins. The "edge" is the stop placement level based on actual measured experience.

Chapter 4: Testing -- Selecting trading vehicles for the campaign. Comparison of perpetual, monthly, and December Crude contracts. MAE analysis across 1984-1994. Key finding: monthly contracts are most profitable, with .31 or .46 MAE stop levels being optimal. Campaign profitability from trend trading alone: 3,600 points.

Chapter 5: Piling On: Exploiting the Trend -- Three types of add-on trades during established trends. Day trades triggered by touches of the short (12-day) moving average. Position trades triggered by touches of the long (60-day) moving average. Each add-on type has its own distinct MAE distribution. Campaign profitability table: trend trading 3,600 + add-on day trades 2,500 + add-on position trades 640 = 6,740 total points.

Chapter 6: Reversing Bad Trades -- When the MAE stop is hit, there is a 90% chance the trade is a loser. Rather than just stopping out, reverse direction. This produces a 70% win rate on reversals (68 winners, 31 losers). Four categories of reversals: mistaken reversals (getting right with the market), false trend signals (quickly reversed), no movement trades, and sharp trend reversals. Reversal profitability adds 1,827 points to the campaign.

Chapter 7: Switching Modes: Trading Ranges -- Recognizing ranges by the absence of a trend. Range definition requires four conditions: (1) a trend lasting at least 6 days, (2) the trend broken by price or average crossing, (3) a reaction level within the cyclic period, and (4) price recrossing the average. Range trading MAE shows wider distributions than trend trading MAE. Range trading adds 550 points to the campaign.

Chapter 8: Reversing Out of Ranges -- Breakout reversals triggered at the .31 stop level. The optimal stop for range reversals is around .51. Favorable excursion on range breakouts tends to equal or exceed the size of the range itself. Range reversals add 2,229 points to the campaign.

Chapter 9: Minimum Favorable Excursion -- MinFE measures the least favorable movement a trade achieves before being closed. MinFE proves better than MaxFE for distinguishing winners from losers. Protective stop placement rule: move stop to breakeven when MinFE exceeds .3. Day-by-day profit pattern analysis reveals when trades typically become profitable. Full campaign profitability across all 7 tactics: 11,346 points.

Chapter 10: Shifting the Odds: Using Options -- Options as flexible alternatives to hard stops. A purchased call is equivalent to being long plus holding a put for protection. Cost comparison shows options are competitive with hard stops when the trade horizon is 10-20 days. MAE knowledge helps select appropriate strike prices because you know how far trades typically move against you.

Chapter 11: The Toolbox -- Assembling the complete campaign. All seven tactics working together as an integrated system. Summary of how trend following, add-on day trades, add-on position trades, trend reversals, range trading, range reversals, and MinFE-based stop advancement combine into a unified campaign approach.

Key Frameworks Referenced

• MAE Distribution Analysis (7-step binning process for empirical stop placement)
• Two-Moving-Average Trend System (12-day short, 60-day long)
• Campaign Profitability Summary Table (all modes, rules, stops, and profit contributions)
• Minimum Favorable Excursion (MinFE) for protective stop advancement
• Options as stop alternatives (cost comparison framework)

Practical Takeaways

1. Measure everything from the point of entry -- not from arbitrary reference points, indicators, or support/resistance levels.
2. Use MAE distributions to set stops empirically: graph how far winners vs. losers move against you and place your stop where losers dominate.
3. When your stop is hit, consider reversing rather than simply exiting -- the 90% loser probability means the other direction is likely correct.
4. Coordinate multiple simple tactics into a campaign rather than seeking one perfect system; each tactic captures profits the others miss.
5. Recognize that markets alternate between trending and ranging modes, and deploy different tactics for each mode.
6. Add-on trades during trends (both day trades and position trades) can nearly double the profitability of trend following alone.
7. Use MinFE to determine when to advance protective stops -- move to breakeven only after the trade has shown sufficient favorable movement.
8. Range breakout reversals can be among the most profitable tactics because favorable excursion tends to equal or exceed range size.
9. Options become cost-competitive with hard stops for trade horizons of 10-20 days and can provide more flexible risk management.
10. Test all tactics on the same data with the same measurement framework (MAE/MinFE) so you can directly compare and combine them.

Who Should Read This

Futures traders looking for a systematic, empirically grounded approach to stop placement and campaign-style position management. System developers who want to move beyond arbitrary stop levels and test multiple coordinated tactics. Anyone interested in MAE/MinFE as analytical tools for understanding trade risk profiles and optimizing exits.