Introduction to HFT Involvement in financial markets
Market Making algorithms continuously post buy (bid) and sell (ask) limit orders, aiming to earn the bid- ask spread while providing liquidity. HFT market makers function much like traditional market makers but at much higher speed and scale. They simultaneously quote both sides of the market and profit from the small price differential (spread) between bids and asks . Exchanges often incentivize market makers via liquidity rebates (fractions of a penny per share) for adding liquidity, which HFT firms capitalize on through very large trading volumes. Over the past two decades, human specialists have largely been supplanted by automated HFT market makers, which now dominate liquidity provision. Limit orders are prioritized over market orders, which in some capacity is fair; however special order types made for HFT that are just slightly more advanced limit orders are prioritized over regular limit orders but are not accessible by retail investors is very unfair. If you search up the IEX exchange website and look at their order types you can see that they are targeting HFT system creators and retail isn't able to use those products themselves.
Statistical Arbitrage (Stat Arb) strategies use quantitative models to exploit pricing inefficiencies among related instruments. In traditional stat arb, a trading system might track a portfolio of securities and identify when price relationships deviate from statistical norms, betting on convergence. In an HFT context, stat arb often means very short-term, high-speed versions of these tactics, sometimes called “data-driven” or “quantitative” HFT strategies. These strategies are market-neutral and typically involve trading a long-short portfolio of correlated assets based on mean reversion expectations. For example, an HFT stat-arb algorithm might monitor price spreads between an ETF and its underlying basket of stocks, or between index futures and the index’s component equities, and rapidly trade to exploit divergences before they close. One common HFT arbitrage is index arbitrage – comparing the real-time value of an index future (say the S&P 500 E-mini) with the aggregated price of the index’s constituent stocks. If the future is temporarily overpriced relative to the underlying, the algo will sell the future and buy the basket (or vice versa), capturing a virtually risk-free profit as prices realign. This is a relatively difficult strategy but is also the strategy I primarily focus on building since as someone who lacks resources, it is the one thing I can make, test, and deploy from my computer.
Latency Arbitrage strategies exploit tiny timing differences in information arrival between trading venues or market participants. If one market moves slightly before another, an HFT latency-arb algorithm can capitalize by racing to trade on the slower venue with knowledge of the price change that is about to occur. In essence, this is a race condition: the fastest trader to react to new information can “pick off” resting orders on venues that have not yet updated their prices. Latency arbitrage is often considered a predatory strategy – it’s been described as effectively front-running public information by virtue of speed. Virtually every time you place a trade through a broker, one of these systems will profit off you buy being payed a maker taker fee for bringing you to an exchange; this isn't fair since in many cases you'll be brought to the BATS exchange which pays the taker and charges the maker (every other exchange does the opposite) and you'll still be stuck paying a commission that someone else profits from.
Event-Driven HFT strategies focus on trading around news or specific trigger events. These algorithms rapidly analyze real-time news feeds, economic data releases, earnings reports, or even social media, and execute trades before human traders can react. In the modern market, machine-readable news feeds and NLP (Natural Language Processing) models are integrated directly into some HFT systems. For example, an event-driven algo might parse a company’s earnings press release the instant it appears and decide to buy or sell the stock based on whether results beat or miss expectations – all within milliseconds. HFT firms subscribe to low-latency news services and sometimes even co-locate servers near newswire sources to get a time advantage in receiving the information. This is self explanatory, just whatever system can react to news the fastest wins.
Some of the more controversial HFT strategies involve detecting liquidity and exploiting it, or conversely avoiding adverse selection from toxic order flow. Liquidity detection algorithms (sometimes called “pinging” strategies) attempt to sniff out hidden orders and large trading interest in the market. HFT firms may send out a flurry of tiny orders – often immediate-or-cancel (IOC) orders for 100 shares or 1 lot – across different price levels or venues to probe for supply or demand. If these pinging orders get hits (even partial fills), it indicates the presence of a large buyer or seller hidden in the order book or a dark pool. The HFT’s algorithm can then escalate its activity: for example, if a ping reveals a big buyer in a dark pool, the HFT might buy up shares on other venues and then sell into the buyer’s demand at a higher price. I'd compare this to playing call of duty and throwing a stun grenade into a room before entering, if someone gets stunned and you get +25 points, you know to move in immediately, and if you don't stun someone, you may proceed with caution.
Momentum Ignition refers to a strategy where a trader deliberately attempts to ignite a rapid price movement – up or down – and capitalize on the frenzy that follows. An HFT or algorithmic trader employing momentum ignition will initiate a series of aggressive orders (market orders or large trades) in a short span, hoping to spark other algorithms or investors to also start buying or selling, thereby driving the price further in that direction. Once momentum takes hold, the instigator can profit by flipping their position (for example, buying shares to start an up-move, then selling into the rally they created). This is what happens every time you see newsa drop and the price of the stock goes in the opposite direction for a split second
Spoofing and Layering are abusive tactics where a trader places non-genuine orders to mislead other market participants about supply or demand, with the intent to cancel those orders before execution. They are illegal in most jurisdictions (for example, spoofing was explicitly outlawed in the US via the 2010 Dodd- Frank Act). These tactics deserve discussion both to understand how rogue algorithms might attempt them and how modern systems detect and prevent them. Currently an order can be cancelled in 0.5 seconds after it has been placed legally.
In essence all of these are ways of fooling retail and institutional investors to profit off their lack of knowledge; I think its important to be informed hence I am pointing this out and publicizing it. There are 100's of pages on the federal registry that will tell you how HFT algorithms are screwing you (the retail investor) over and with all that being said I believe that investing should be left to computers as they have taken over the market. It isn't fair but it's how the world works now. You are just liquidity swimming in pools for larger fish to feed off.
Market Making algorithms continuously post buy (bid) and sell (ask) limit orders, aiming to earn the bid- ask spread while providing liquidity. HFT market makers function much like traditional market makers but at much higher speed and scale. They simultaneously quote both sides of the market and profit from the small price differential (spread) between bids and asks . Exchanges often incentivize market makers via liquidity rebates (fractions of a penny per share) for adding liquidity, which HFT firms capitalize on through very large trading volumes. Over the past two decades, human specialists have largely been supplanted by automated HFT market makers, which now dominate liquidity provision. Limit orders are prioritized over market orders, which in some capacity is fair; however special order types made for HFT that are just slightly more advanced limit orders are prioritized over regular limit orders but are not accessible by retail investors is very unfair. If you search up the IEX exchange website and look at their order types you can see that they are targeting HFT system creators and retail isn't able to use those products themselves.
Statistical Arbitrage (Stat Arb) strategies use quantitative models to exploit pricing inefficiencies among related instruments. In traditional stat arb, a trading system might track a portfolio of securities and identify when price relationships deviate from statistical norms, betting on convergence. In an HFT context, stat arb often means very short-term, high-speed versions of these tactics, sometimes called “data-driven” or “quantitative” HFT strategies. These strategies are market-neutral and typically involve trading a long-short portfolio of correlated assets based on mean reversion expectations. For example, an HFT stat-arb algorithm might monitor price spreads between an ETF and its underlying basket of stocks, or between index futures and the index’s component equities, and rapidly trade to exploit divergences before they close. One common HFT arbitrage is index arbitrage – comparing the real-time value of an index future (say the S&P 500 E-mini) with the aggregated price of the index’s constituent stocks. If the future is temporarily overpriced relative to the underlying, the algo will sell the future and buy the basket (or vice versa), capturing a virtually risk-free profit as prices realign. This is a relatively difficult strategy but is also the strategy I primarily focus on building since as someone who lacks resources, it is the one thing I can make, test, and deploy from my computer.
Latency Arbitrage strategies exploit tiny timing differences in information arrival between trading venues or market participants. If one market moves slightly before another, an HFT latency-arb algorithm can capitalize by racing to trade on the slower venue with knowledge of the price change that is about to occur. In essence, this is a race condition: the fastest trader to react to new information can “pick off” resting orders on venues that have not yet updated their prices. Latency arbitrage is often considered a predatory strategy – it’s been described as effectively front-running public information by virtue of speed. Virtually every time you place a trade through a broker, one of these systems will profit off you buy being payed a maker taker fee for bringing you to an exchange; this isn't fair since in many cases you'll be brought to the BATS exchange which pays the taker and charges the maker (every other exchange does the opposite) and you'll still be stuck paying a commission that someone else profits from.
Event-Driven HFT strategies focus on trading around news or specific trigger events. These algorithms rapidly analyze real-time news feeds, economic data releases, earnings reports, or even social media, and execute trades before human traders can react. In the modern market, machine-readable news feeds and NLP (Natural Language Processing) models are integrated directly into some HFT systems. For example, an event-driven algo might parse a company’s earnings press release the instant it appears and decide to buy or sell the stock based on whether results beat or miss expectations – all within milliseconds. HFT firms subscribe to low-latency news services and sometimes even co-locate servers near newswire sources to get a time advantage in receiving the information. This is self explanatory, just whatever system can react to news the fastest wins.
Some of the more controversial HFT strategies involve detecting liquidity and exploiting it, or conversely avoiding adverse selection from toxic order flow. Liquidity detection algorithms (sometimes called “pinging” strategies) attempt to sniff out hidden orders and large trading interest in the market. HFT firms may send out a flurry of tiny orders – often immediate-or-cancel (IOC) orders for 100 shares or 1 lot – across different price levels or venues to probe for supply or demand. If these pinging orders get hits (even partial fills), it indicates the presence of a large buyer or seller hidden in the order book or a dark pool. The HFT’s algorithm can then escalate its activity: for example, if a ping reveals a big buyer in a dark pool, the HFT might buy up shares on other venues and then sell into the buyer’s demand at a higher price. I'd compare this to playing call of duty and throwing a stun grenade into a room before entering, if someone gets stunned and you get +25 points, you know to move in immediately, and if you don't stun someone, you may proceed with caution.
Momentum Ignition refers to a strategy where a trader deliberately attempts to ignite a rapid price movement – up or down – and capitalize on the frenzy that follows. An HFT or algorithmic trader employing momentum ignition will initiate a series of aggressive orders (market orders or large trades) in a short span, hoping to spark other algorithms or investors to also start buying or selling, thereby driving the price further in that direction. Once momentum takes hold, the instigator can profit by flipping their position (for example, buying shares to start an up-move, then selling into the rally they created). This is what happens every time you see newsa drop and the price of the stock goes in the opposite direction for a split second
Spoofing and Layering are abusive tactics where a trader places non-genuine orders to mislead other market participants about supply or demand, with the intent to cancel those orders before execution. They are illegal in most jurisdictions (for example, spoofing was explicitly outlawed in the US via the 2010 Dodd- Frank Act). These tactics deserve discussion both to understand how rogue algorithms might attempt them and how modern systems detect and prevent them. Currently an order can be cancelled in 0.5 seconds after it has been placed legally.
In essence all of these are ways of fooling retail and institutional investors to profit off their lack of knowledge; I think its important to be informed hence I am pointing this out and publicizing it. There are 100's of pages on the federal registry that will tell you how HFT algorithms are screwing you (the retail investor) over and with all that being said I believe that investing should be left to computers as they have taken over the market. It isn't fair but it's how the world works now. You are just liquidity swimming in pools for larger fish to feed off.
Note
In reality IEX isn't actually targeting HFT systems in a malicious way, rather doing everything they can to stop these bots from exploiting holes in the markets infrastructure making it as fair for people as they can
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The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.