Today markets are moving faster and faster , many retail and also institutional are finding many trouble because they don’t really know how does it work the system.
Being institutional doesn’t mean be strong, in order to map trading algorithms we would need to determine estimates for each of their expected costs and timing risks and then overlay these on an efficient trading frontier for the perspective order.
Key words: percent of volume, price inline, adaptive shortfall, implementation shortfall, liquidity driven, market to close and minimal impact.
Impact driven algorithms tend to appear towards the right hand side of the efficient trading frontier, let see some examples below:
Vwap algorithm concentrates on minimizing market impact by splitting the order into quantities based on the historical volume profile. Vwap order left to trade through the whole day has one of the highest associated risks.
Twap when the algorithm split an order it does not take into account market conditions, so it may incur additional market impact compared to other algorithms.
Percent of volume ( POV) is quite similar to Vwap , the only differenc between the two approaches is that trading trajectory for a POV algorithm is generated dynamically based on a fixed proportion of the actual market volume.
Minimal impact by focusing solely on minimasing the overall market cost, these algorithms take on a highr level of timing risk. Consequently they appear on the right hand side of the efficient trading frotier, these algorithms attempt to balance cost and risk.
Implementation shortfall these algorithms seek to minimize both market impact and risk, often by determining the optimal rate of trading. They tend to trade more quickly than Vwap or Pov algorithms resulting in a lower timing risk.
Market to close these algorithms aim to match or better the future closing price implementation shortfall algorithm calculates an optimal trade duration.
Adaptive shortfall an aggressive adaptive shortfall algorithm becomes more aggressive with favourable prices, the opportunistic algorithms are more difficult to place on the efficient trading frontier, they can take advantage of favourable conditions, this can lead the substantial impact costs.
Price incline the main price adaptation takes advantage of better market conditions, achieving a lower expected cost but exposing it to higher overall risk.
Liquidity driven algorithm will trade fairly aggressively when there is liquidity but at the other times it simply will not trade.
Pairs trading algorithm has a degree of inbuilt hedging, provided the relationship between the 2 asset prices behaves as we expect, so gains or losses on asset B should hopefully offset any losses or gains on asset.