Free Crypto Algo Trading Strategies: 6 Rule Sets Explained
Six free crypto algo strategies with exact entry, exit and stop rules, from MA crossover to grid and DCA, plus how fees and 1% TDS eat returns in India.
The best-known free crypto algo trading strategies are six simple rule sets that anyone can use: moving-average crossover, RSI mean reversion, Donchian channel breakout, grid trading, dollar-cost averaging (DCA) and a funding-rate filter for futures. The rules are public and cost nothing, but none of them has a guaranteed edge. Most retail strategies lose money after fees, so each one must be backtested and paper traded before you risk real rupees.
Key takeaways
- A complete strategy defines the market, the entry, the exit, the stop-loss, the position size and when not to trade.
- Trend strategies (crossover, breakout) suit trending markets; mean reversion and grids suit sideways markets. Each fails in the other.
- Size every trade from the stop: with ₹200 of risk and a 2% stop, the position is ₹10,000.
- On Indian spot exchanges, 1% TDS is deducted on each sell. 100 sells of ₹10,000 lock ₹10,000 in your tax account until you claim it back.
- No free or paid strategy guarantees profit. Backtests overstate results, and automation repeats mistakes as faithfully as good rules.
What a complete algo strategy must define
An algo strategy is a set of rules precise enough for a computer to follow without asking you anything. If a rule needs judgement ("buy when it looks strong"), it is not ready for a bot. Before any code, write down:
- Market and timeframe: which coin or pair, spot or futures, and which candle size (15 minutes, 4 hours, daily).
- Entry: the exact condition that opens a trade.
- Exit: the condition that closes a winning or flat trade.
- Stop-loss: the price at which you accept you were wrong. It should sit on the exchange as an order, not only inside your bot.
- Position size: worked out from the stop, never from a feeling.
- Filter: when the strategy should stay out, for example in a strong downtrend or ahead of big scheduled news.
Our guide to how crypto trading bots place trades shows how these rules turn into signed orders on an exchange.
Six free strategies at a glance
| Strategy | Entry | Exit | Stop-loss | Suits | Main weakness |
|---|---|---|---|---|---|
| Moving-average crossover | 20-period EMA closes above the 50-period EMA | 20 EMA closes back below the 50 EMA | Below the most recent swing low | Trending markets | Whipsaws and late entries in sideways markets |
| RSI mean reversion | 14-period RSI closes below 30 | RSI rises back above 50, or a fixed target | A fixed percentage below entry | Sideways, ranging markets | RSI can stay below 30 for weeks in a crash |
| Donchian breakout | Close above the highest high of the last 20 candles | Close below the lowest low of the last 10 candles | The 10-candle low, or a multiple of recent average range | Strong trends | Many false breakouts and long losing streaks |
| Grid | Buy orders at fixed steps below the price | Sell one step above each filled buy | Below the bottom of the grid range | Sideways ranges | Trends that leave the range; fees on small steps |
| DCA | Buy a fixed ₹ amount at a fixed interval | None; a long-term hold or a planned target | Usually none, by design | Long-term accumulation | No protection in a long bear market; not a trading edge |
| Funding-rate filter | Only hold perpetual futures when funding is not heavily against you | Reduce or close before paying high funding again and again | The main strategy's stop | Futures positions held for days | A filter, not a signal; arbitrage versions carry extra risk |
The numbers in the table (20 and 50, 14 and 30, 20 and 10) are common textbook defaults, not optimised settings. Tweaking them until a backtest looks perfect is called overfitting, and it is the fastest way to build a strategy that fails live.
The rules in plain words
1. Moving-average crossover
A moving average smooths price by averaging the last few candles. An exponential moving average (EMA) gives recent candles more weight. When the fast average (20) crosses above the slow one (50), recent prices are rising faster than the longer trend, and the bot buys. When it crosses back below, the bot sells. On a spot exchange you can only go long, so the exit is simply a sell back to rupees. Many traders add a filter, such as taking longs only when price is above its 200-period average. The strategy catches big trends but gives back part of each move, and in a flat market it buys and sells repeatedly for small losses.
2. RSI mean reversion
The Relative Strength Index (RSI) measures the speed of recent moves on a 0 to 100 scale. Below 30 is called oversold. The strategy bets that a sharp fall will partly bounce back, so it buys when RSI closes below 30 and exits when RSI recovers to 50. It tends to win often with small gains, which feels good until a crash, when RSI stays low while price keeps falling. That is why the stop matters most here. Never let a bot "average down" by buying more each time RSI stays oversold.
3. Donchian channel breakout
A Donchian channel draws the highest high and lowest low of the last N candles. The strategy buys when price closes above the 20-candle high and exits when it closes below the 10-candle low. It is a pure trend-following rule: most trades are small losses from false breakouts, and a few large trends pay for them. The hard part is psychological. Long losing streaks are normal, and people often switch the bot off just before the trend that would have paid for them.
4. Grid trading
A grid places buy orders at fixed steps below the current price and a sell order one step above each buy that fills. It earns a small amount each time price moves up and down between levels, and it suffers when price trends out of the range. Grid bots are the most common built-in exchange bot, so they have their own guide: what a grid trading bot is and how it works.
5. Dollar-cost averaging (DCA)
DCA buys a fixed rupee amount at fixed intervals, whatever the price, for example ₹1,000 of bitcoin every week. It is an accumulation method, not a trading edge: it smooths your average price but does not protect you if a coin falls for years. Our guides to dollar-cost averaging in crypto and starting a crypto SIP in India cover it in depth. Be careful with "DCA bots" that buy more each time price falls, especially with leverage: that is averaging down, and it can wipe out an account in one long decline.
6. Funding-rate awareness for futures
Perpetual futures use a funding rate to keep their price close to spot. When funding is positive, long positions pay shorts; when negative, shorts pay longs. Payments happen at fixed intervals set by each exchange, so check the contract details page. A leveraged long held for weeks while funding is high and positive slowly bleeds money even if price goes nowhere. The rule is simple: check funding before entering, avoid holding a position that pays heavy funding again and again, and include funding in every backtest. Our guide on what the funding rate is explains the maths.
"Funding-rate arbitrage" (buying spot and shorting the perpetual to collect funding) is sold as low risk. In India it is not simple: the spot sell attracts 1% TDS, a loss on one crypto asset cannot reduce the taxable profit on another, and the tax treatment of futures is still unsettled, as our guide to crypto futures tax in India explains. A hedge that breaks even before tax can lose after it.
Position sizing: size every trade from the stop
The size of a trade should come from how much you are willing to lose if the stop is hit, not from how confident you feel. The formula is: position size = rupees at risk ÷ stop distance. With ₹200 at risk:
| Stop distance from entry | Position size | Loss if the stop is hit |
|---|---|---|
| 1% | ₹20,000 | ₹200 |
| 2% | ₹10,000 | ₹200 |
| 5% | ₹4,000 | ₹200 |
| 10% | ₹2,000 | ₹200 |
Fees, slippage and gaps can make the real loss slightly larger, so leave a margin. Our guides to risk-first position sizing and using a stop-loss in crypto go further. Leverage does not change the maths: if the position is sized from the stop, leverage only changes how much margin you post, but it does add liquidation risk.
How fees and 1% TDS eat a strategy: a ₹ example
This illustrative example assumes 100 round trips on an INR spot pair, each a ₹10,000 buy followed by a ₹10,000 sell, and an assumed trading fee of 0.2% per order. Check your own exchange's fee schedule.
| Item | How it is worked out | Amount |
|---|---|---|
| Total sold | 100 sells of ₹10,000 | ₹10,00,000 |
| TDS deducted on sells | 1% of ₹10,00,000 | ₹10,000 moved into your tax account |
| Trading fees | 0.2% of ₹20,00,000 across 200 orders | ₹4,000 |
| GST on fees | 18% of ₹4,000 | ₹720 |
| Total fees and GST | ₹4,000 plus ₹720 | ₹4,720 |
| Break-even per round trip | ₹4,720 ÷ 100 trades | ₹47.20, or 0.47% of each trade |
A strategy that averages 0.4% gross per trade therefore loses money in this example, and slippage makes it worse (see what slippage is). The ₹10,000 of TDS is not an extra tax: it is credited against your final tax, and any excess is refunded after you file. But it is cash you cannot trade with until then. For someone trading with ₹50,000, that is a fifth of the account sitting with the tax department. Crypto-to-crypto trades are worse: CoinDCX, for example, deducts 1% TDS on both sides of non-INR pairs. Then gains are taxed at a flat 30% plus 4% cess, with no set-off of losses against other income and no carry-forward. Read how 1% TDS on crypto works before you run any high-frequency rule on spot.
Why free strategies rarely make money
- Everyone has them. Rules printed in every textbook are traded by thousands of bots, so any easy edge is quickly competed away.
- Costs are certain, profits are not. Fees, GST, slippage and funding apply to every trade; profits only come from some of them.
- Overfitting. Settings tuned to past data describe the past, not the future.
- Markets change regime. A crossover that worked in a trending year bleeds in a sideways one, and a grid that worked in a range breaks in a trend.
- Execution is messy. Outages, partial fills, API errors and duplicated orders never appear in a clean backtest.
- People interfere. Many traders switch a bot off after a losing streak and back on after a winning one, which is the worst possible timing.
Be wary of anyone selling a "secret" strategy with a claimed return. SEBI has barred stock brokers from letting algo providers advertise returns and has cautioned investors against "live trading strategies" promoted on social media. Those rules cover securities, but the lesson applies fully to crypto.
How to test a strategy before risking money
- Write the rules down completely, including fees, the stop and the size.
- Backtest across different markets: a rising year, a falling year and a sideways one. Include fees, GST, slippage and funding. Our guide on how to backtest a crypto trading strategy shows free ways to do this.
- Use a large enough sample. A result from 15 trades is mostly luck. Treat about 100 trades as a sensible minimum before you trust a win rate or an average, and remember that even 100 trades guarantees nothing.
- Paper trade for several weeks on a demo account or testnet, with the same rules and size.
- Go live small, with a fixed monthly loss limit, and compare live results with the backtest every month.
If you want to practise rule-following with a fixed loss before automating anything, our free 100-Day Trade Challenge gives up to two AI-generated trade ideas a day with entry, stop-loss and take-profit levels, sized so one stop costs about ₹200 on the default wallet. You place each trade yourself on your own exchange, and there is no guarantee of profit. When you are ready to automate, our guide to free crypto trading bots in India covers the tools, and the beginner's guide to crypto algo trading in India sets out the full roadmap.
FAQ
What is the best free algo trading strategy for crypto?
There is no best strategy for all markets. Trend rules such as moving-average crossovers and breakouts suit trending markets, while RSI mean reversion and grids suit sideways ones, and each loses money in the other kind of market.
Which crypto algo strategy is the most profitable?
None is reliably profitable, and most retail strategies lose money after fees. Any claim of a fixed return from a strategy or bot is a warning sign, not a feature.
Where can I get crypto algo trading strategies for free?
The classic rule sets in this guide are public knowledge and free to use. Open-source bot projects also publish example strategies, but every one needs backtesting and paper trading before real money.
What is a simple trading bot strategy for beginners?
A moving-average crossover on 4-hour or daily candles, with a stop below the last swing low and a position sized so one loss costs a small fixed amount, is easy to understand and to code. Simple does not mean profitable, so test it first.
Do I need coding skills to use algo strategies?
Not always. Exchange built-in bots and no-code platforms let you run grids, DCA or alert-based rules without code, but custom strategies on most Indian exchanges need some programming.
How does 1% TDS affect algo trading in India?
Indian exchanges deduct 1% TDS on each qualifying crypto sale, so a spot bot that sells often locks up a large amount of cash until you claim it back through your tax return. CoinDCX and Delta Exchange India say TDS does not apply to their futures, but the tax treatment of futures is not yet settled.
This article is AI-assisted, educational and general in nature. It is not financial advice and never a guarantee of profit. Every trade is at your own risk on your own exchange. See our risk disclosure and editorial policy.
