Automating Your Strategies with Predite's Bot Builder
Why Automate at All
Manual trading on Polymarket and Kalshi has a hard ceiling: you can only watch so many markets, and the best edges — a whale loading up on a contract, a fresh mispricing between two correlated markets, a news event that hasn't propagated into prices yet — tend to appear when you're asleep, in a meeting, or staring at a different market entirely. The Bot Builder exists to close that gap. It turns the strategies you already run by hand into rule-based agents that watch the order book around the clock, size positions consistently, and never get bored, greedy, or scared.
Automation is not a shortcut to skipping the thinking. A bot is only as good as the logic and the guardrails you give it. This guide walks through the six templates, how to configure them safely, the execution model that runs them, and the discipline — paper-first, backtest-first, monitor-always — that separates traders who compound from traders who blow up. The Bot Builder is part of the Bot plan ($99/mo); the EV and analytics features it depends on live on Pro ($59) and Starter ($29), but live and paper bot execution is Bot-only.
The Six Templates
Every bot starts from a template. A template is a pre-wired strategy skeleton — it defines what signal the bot looks for and how it translates that signal into an order. You then tune the parameters. Picking the right template is the single most important decision, because each one assumes a different kind of market inefficiency.
EV Follower
The workhorse. The EV Follower watches Predite's fair-value model and opens a position whenever the model's estimated probability diverges from the market price by more than your minimum edge. If the model says a contract is worth 0.62 and it's trading at 0.55, that's a 7-cent edge — buy. This template works best in liquid, frequently-repriced markets (major elections, Fed decisions, sports) where the model has enough data to be calibrated and where you can actually fill at the quoted price. It struggles in thin, idiosyncratic markets where the "edge" is really just model noise.
ARB Hunter
The ARB Hunter looks for logically linked markets that don't add up. The classic case: a multi-candidate election where the individual "Yes" prices sum to more than 1.00 (a guaranteed-profit short basket) or a market on one venue priced differently from the equivalent market on the other. It also catches Yes/No pairs on the same contract that sum to less than 1.00. Arbitrage edges are small (often 1–3 cents) but high-conviction, so this template pairs naturally with larger size and tight risk limits. Its enemy is execution risk: if you can only fill one leg, you're no longer arbitraged, so the bot is built to require both legs or skip.
Whale Copier
This template subscribes to Predite's whale-tracking feed and mirrors the entry of wallets you've flagged — large, historically profitable accounts. When a tracked wallet takes a position above a size threshold, the bot opens a scaled-down version. Whale Copier shines when you've curated a short list of genuinely sharp wallets and want their conviction without watching the chain yourself. It fails when you copy indiscriminately: not every large wallet is smart money, and some are hedging positions you can't see. Use the whale's win-rate history to filter before you copy.
Mean Reversion
Mean Reversion fades sharp, unexplained price moves, betting the contract snaps back toward its recent average. If a market sitting at 0.40 for a week spikes to 0.52 on no news, the bot shorts the spike expecting a return to the mean. This is powerful in range-bound, low-information markets but genuinely dangerous around real events — the whole point of a news-driven move is that the mean has changed. Always pair this template with a News Reactor or a calendar filter so you aren't fading legitimate repricings.
News Reactor
The mirror image. News Reactor ingests Predite's headline and sentiment stream and trades in the direction of a move when fresh information lands, aiming to be early before the order book fully adjusts. It works in markets that are demonstrably news-sensitive (geopolitics, crypto-adjacent contracts, company-specific Kalshi markets). The risk is speed and noise: headlines get revised, sentiment scoring is imperfect, and a 15-minute execution cadence (more on that below) means you're not competing with millisecond bots — you're capturing the slower repricing that plays out over hours.
Calendar Bot
Calendar Bot is time-triggered rather than price-triggered. You tell it to act relative to a known scheduled event — open a position 48 hours before an earnings print, close everything 30 minutes before a resolution, or enter a recurring weekly market every Monday. It's ideal for strategies where *timing* is the whole edge: pre-event positioning, end-of-cycle exits, or rolling into a new contract series. It does nothing clever about price; it executes a schedule reliably, which is exactly what you want it to do.
Configuring Parameters
Templates give you the logic; parameters give you the risk profile. Six controls matter most, and they interact.
- •Minimum edge — the smallest divergence that triggers a trade. Set it too low (say 1 cent) and you'll fire constantly on noise and bleed to fees and slippage; set it too high (12+ cents) and you'll almost never trade. For an EV Follower on liquid markets, 4–6 cents is a sane starting band. ARB Hunter runs much tighter because the edge is near-certain.
- •Max size — the largest stake per position, in dollars or as a percent of bankroll. This is your blast radius. A common pattern is fractional Kelly: compute the Kelly-optimal size from your edge and odds, then take a quarter or half of it. Predite can surface the Kelly figure; resist the urge to bet full Kelly, which is mathematically aggressive and assumes your edge estimate is perfect.
- •Stop-loss — the adverse move at which the bot exits a losing position. Prediction markets are bounded (0 to 1), so a stop is less about ruin and more about freeing capital and admitting the thesis broke. A stop at, say, 15 cents against entry caps the damage on any single bad call.
- •Take-profit — the favorable level at which the bot banks the win. Setting this matters because contracts can round-trip; locking in at 0.85 a position you entered at 0.55 realizes the gain rather than riding it back down.
- •Daily limits — a cap on number of trades, total capital deployed, or realized loss per day. This is your circuit breaker against a bad day compounding. A daily max-loss that pauses the bot after, e.g., 3% of bankroll is one of the most valuable settings you can configure.
- •Slippage tolerance — the maximum price degradation the bot will accept versus the quote that triggered it. If the book moves past this between signal and fill, the bot skips. On thin markets this single setting prevents most "why did it fill 6 cents worse?" disasters.
A worked example. Suppose you run an EV Follower with a $5,000 paper bankroll, min edge 5 cents, max size $150 (3% of bankroll), stop-loss 15 cents, take-profit at model-fair-value-minus-2-cents, daily trade limit 8, daily max-loss $150, slippage tolerance 2 cents. That's a deliberately conservative profile: small positions, a hard daily floor, and a refusal to chase fills. It will trade rarely and lose slowly even when wrong — which is exactly what you want while you're learning whether the edge is real.
The 15-Minute Cron Execution Model
Predite bots do not run as a continuous high-frequency loop. They execute on a 15-minute cron cycle: every quarter hour, the platform wakes each active bot, pulls fresh market data and signals, evaluates the bot's rules, and places, adjusts, or closes orders accordingly. Between cycles, the bot is dormant.
This design is deliberate and has real consequences you should internalize:
- •You are not in a latency race. A 15-minute cadence means you will never beat a co-located market-making bot to a fleeting microsecond mispricing. That's fine — Predite's templates target inefficiencies that persist for minutes to hours (model divergences, whale entries, news repricing, calendar timing), not microstructure scalps.
- •Signals must survive the gap. An edge that exists at 10:00 and is gone by 10:07 won't be caught until 10:15, when it's stale. Strategies that depend on instantaneous reaction are the wrong fit. Strategies that exploit the *slow* propagation of information are the right fit.
- •Execution is checkpointed. Because evaluation is discrete, your stop-loss and take-profit are checked at each cycle, not tick-by-tick. A contract can spike past your stop and recover within one cycle and the bot may never act on it — or may act at the next cycle's price. Size your stops with that granularity in mind.
The cron model also makes the system predictable and auditable, which is what you want when real money is on the line. You always know when decisions happen, and every cycle is logged.
Paper-First Discipline
Never run a new bot live. Every bot in Predite can run in paper mode, where it executes against real market prices with simulated capital. Paper mode is not a toy; it's the proving ground. It runs the identical logic, the identical cron cycle, and records the identical metrics — it just doesn't touch your funds.
The discipline is simple to state and hard to follow: run every new configuration in paper for a meaningful sample before risking a cent. "Meaningful" means enough trades that the results aren't luck — as a rule of thumb, at least 30–50 closed positions, and ideally across different market conditions. A bot that looks brilliant over six trades has told you almost nothing. Watch specifically for the gap between *expected* and *realized* results: paper mode will reveal slippage and skip behavior that a backtest can hide.
Backtesting Before Live
Paper mode tests the future, forward, slowly. Backtesting tests the past, instantly. Before you even paper-trade, run the configuration against historical data to get a first read on whether the logic has ever made money. Predite's backtester replays past prices and signals through your exact parameters and reports the strategy's hypothetical P&L, win rate, and drawdown.
Use backtesting to kill bad ideas cheaply and to tune parameters, but treat its results with suspicion:
- Overfitting is the enemy. If you tweak min edge, stop, and take-profit until the backtest looks perfect, you've likely fit the parameters to historical noise. They won't generalize. Prefer round, defensible numbers over hyper-optimized ones.
- The past underrepresents slippage. Backtests fill at historical quotes; live markets move against you. Always assume real performance is somewhat worse than the backtest.
- Regime changes break everything. A Mean Reversion bot that thrived in a quiet quarter can get shredded in a volatile one. A backtest spanning only calm conditions is misleading.
The healthy sequence is backtest → paper → small live → scale. Backtest to filter, paper to validate forward, go live small to confirm execution behaves, and only then increase size.
Monitoring What Matters
A live bot is not "set and forget." Predite gives you a metrics dashboard per bot; four numbers deserve your regular attention.
- •Win rate — the share of closed positions that were profitable. Watch the *trend*, not just the level. A high win rate with tiny wins and rare huge losses can still be unprofitable; a 45% win rate with good payoff asymmetry can be excellent. Read it alongside P&L.
- •Slippage — the average gap between the price that triggered each trade and the price you actually got. Rising slippage means you're either trading markets that are too thin for your size or your tolerance is set too loose. It's the silent killer of edges that look fine on paper.
- •Skip reasons — every time the bot evaluates and *doesn't* trade, it logs why: edge below threshold, slippage too high, daily limit hit, insufficient liquidity, both arb legs unavailable. This log is the most underrated diagnostic in the platform. If your bot is skipping 90% of signals on slippage, you've found your problem. If it's skipping nothing and trading constantly, your thresholds may be too loose.
- •Realized vs. expected P&L — compare what the bot actually earned against what its edge implied it should. A persistent gap points to slippage, fees, or a miscalibrated model.
Check these at least daily for a new live bot, and after any market regime shift (a big election resolving, a volatility spike) for established ones.
The Kill Switch
Every Predite bot has a kill switch — a single control that immediately halts all activity. Hitting it stops the bot from placing any new orders on the next cron cycle. This is your emergency brake for when something is clearly wrong: a market behaving bizarrely, a configuration you realize is broken, a news event that invalidates the whole strategy, or simply a string of losses that exceeds your comfort.
Two practices around it. First, know where it is before you need it — don't go hunting for the control while a bot is losing money. Second, lean on the automated daily max-loss limit as your always-on kill switch; the manual button is for the situations your rules didn't anticipate. The combination of a hard automated floor and a human override covers both the predictable and the surprising.
Combining Multiple Bots Without Overlap
Once one bot is working, the temptation is to run several. Diversification across uncorrelated strategies is genuinely powerful — but only if the bots don't step on each other. Overlap creates two hidden risks: concentration (three bots independently piling into the same contract, turning a 3% position into a 9% one) and self-competition (a Mean Reversion bot shorting the exact spike a News Reactor is buying, so you pay spread on both sides and net to nothing).
Manage it deliberately:
- Segment by market or theme. Give each bot a non-overlapping universe — one on politics, one on crypto, one on sports — so they can't collide on the same contract.
- Budget capital per bot. Allocate a fixed slice of bankroll to each so the total deployed is bounded even if every bot fires at once. Three bots at 3% max size each is a very different risk than three bots sharing one 9% pool.
- Avoid logically opposed templates on the same universe. Mean Reversion and News Reactor are natural antagonists; if you must run both, separate their markets or add a news filter so reversion stands down when a real event hits.
- Watch aggregate exposure. Monitor your *combined* position across all bots, not just each in isolation. The portfolio view is what tells you whether your "diversified" setup is secretly one big bet.
Done well, a stable of bots smooths your equity curve: when reversion is having a rough week, arb and calendar strategies carry it. Done carelessly, you've just built one fragile mega-position with extra steps.
Start Small, Then Compound
The Bot Builder rewards patience far more than cleverness. The traders who do well with it aren't the ones who deploy the most elaborate configuration on day one — they're the ones who backtest to filter ideas, paper-trade until the numbers are boringly consistent, go live with tiny size, read their skip reasons religiously, and only scale what has earned the trust. Pick one template that matches a market inefficiency you actually understand, give it conservative limits and a hard daily max-loss, and let it run in paper this week. When the metrics convince you — not before — turn on a small live allocation and watch your first real cron cycles execute. That first validated bot is the foundation everything else builds on, and the Bot plan is where it starts.