Algorithmic trading

Presented By:-
Tushar Rathi
Algorithmic Trading

Introduction
 Algorithmic trading is the process of using computers
programmed to follow a defined set of instructions
for placing a trade in order to generate profits at a
speed and frequency that is impossible for a
human trader.
 The defined sets of rules are based on timing, price,
quantity or any mathematical model.
 Apart from profit opportunities for the trader, algo-
trading makes markets more liquid and makes
trading more systematic by ruling out emotional
human impacts on trading activities.
 It is also known as Quantitative Trading, High
Frequency Trading, Automatic Trading and Black-

Benefits
 Trades executed at the best possible prices
 Instant and accurate trade order placement (thereby high
chances of execution at desired levels)
 Trades timed correctly and instantly, to avoid significant
price changes
 Reduced transaction costs
 Simultaneous automated checks on multiple market
conditions
 Reduced risk of manual errors in placing the trades
 Backtest the algorithm, based on available historical
and real time data
 Reduced possibility of mistakes by human traders based

High Frequency Trading (HFT)
 It is the greatest portion of Algo Trading in present
days
 It attempts to capitalize on placing a large number
of orders at very fast speeds across multiple
markets and
 Multiple decision parameters, based on pre-
programmed instructions
 HFT rely on the ultra fast speed of computer
software, data access to important resources and
connectivity with minimal latency (delay).

Algo-trading is used in…
 Mid to long term investors or buy side firms
(pension funds, mutual funds, insurance companies)
who purchase in stocks in large quantities but do not
want to influence stocks prices with discrete, large-
volume investments.
 Short term traders and sell side participants
(market makers, speculators, and arbitrageurs)
benefit from automated trade execution; in addition,
algo-trading aids in creating sufficient liquidity
for sellers in the market.
 Systematic traders (trend followers, pairs
traders, hedge funds, etc.) find it much more efficient

Algo Trading Strategies
 Trend Following Strategies
 This is the most common strategy which follow
trends in moving averages, channel
breakouts, price level movements and
related technical indicators.
 Arbitrage Opportunities
 Buying a dual listed stock at a lower price in one
market and simultaneously selling it at a higher
price in another market offers the price differential
as risk-free profit or arbitrage
 Implementing an algorithm to identify such price
differentials and placing the orders allows profitable
opportunities in efficient manner.

 Index Fund Rebalancing
 Index funds have defined periods of rebalancing to
bring their holdings to par with their
respective benchmark indices.
 This creates profitable opportunities for algorithmic
traders, who capitalize on expected trades that offer
20-80 basis points profits depending upon the number
of stocks in the index fund, just prior to index fund
rebalancing.
 Such trades are initiated via algorithmic trading
systems for timely execution and best prices
 Mathematical Model Based Strategies
 A lot of proven mathematical models, like the delta-
neutral trading strategy

 Volume Weighted Average Price (VWAP)
 This strategy breaks up a large order and releases
dynamically determined smaller chunks of the order
to the market using stock specific historical volume
profiles
 Trading Range (Mean Reversion)
 Mean reversion strategy is based on the idea that
the high and low prices of an asset are a temporary
phenomenon that revert to their mean value
periodically

 Time Weighted Average Price (TWAP)
 This strategy breaks up a large order and releases
dynamically determined smaller chunks of the order
to the market using evenly divided time slots
between a start and end time.
 Percentage of Volume (POV)
 Until the trade order is fully filled, this algorithm
continues sending partial orders, according to the
defined participation ratio and according to the
volume traded in the markets

 Implementation Shortfall
 This strategy aims at minimizing the execution cost
of an order by trading off the real-time market,
thereby saving on the cost of the order and
benefiting from the opportunity cost of delayed
execution

Technical Requirements for Algo
Trading
 Computer programming knowledge to program
the required trading strategy, hired programmers
or pre-made trading software
 Network connectivity and access to trading
platforms for placing the orders
 Access to market data feeds that will be
monitored by the algorithm for opportunities to
place orders
 The ability and infrastructure to backtest the
system once built, before it goes live on real
markets
 Available historical data for backtesting,
depending upon the complexity of rules
implemented in algorithm

Areas of Concern
 Lack of Visibility
 Algorithms acting on other Algorithms
 Which Algorithm to use?
 Missing Ingredient- Intuitions or Gut Feel.

Algorithmic trading

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Algorithmic trading